{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/victor/.local/lib/python3.6/site-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use \"pip install psycopg2-binary\" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.\n",
      "  \"\"\")\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "import json\n",
    "import time\n",
    "import itertools\n",
    "from scipy import stats\n",
    "import psycopg2 as psql\n",
    "from psycopg2.extras import RealDictCursor\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "from tools.flight_projection import *\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)\n",
    "\n",
    "try:\n",
    "    conn = psql.connect(\"dbname='thesisdata' user='postgres' host='localhost' password='postgres'\")\n",
    "except Exception as e:\n",
    "    print(\"Unable to connect to the database.\")\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def realign_conflict(b):\n",
    "    \n",
    "    cfl_dst = 9260\n",
    "    \n",
    "    for i in range(1,len(b)):\n",
    "        \n",
    "        if calc_coord_dst_simple((b['lon_1'][-i],b['lat_1'][-i]),(b['lon_2'][-i],b['lat_2'][-i])) >= cfl_dst:\n",
    "            for k in ['ts_1','lat_1','lon_1','alt_1','spd_1','hdg_1','roc_1',\n",
    "                      'ts_2','lat_2','lon_2','alt_2','spd_2','hdg_2','roc_2']:\n",
    "                b[k] = b[k][:-(i-1)]\n",
    "            return (b)\n",
    "    \n",
    "    return None\n",
    "        \n",
    "\n",
    "def time_to_conflict(tr1,tr2):\n",
    "    \n",
    "    cfl_dst = 9260\n",
    "    \n",
    "    for i in range(len(tr1)):\n",
    "        cdst = calc_coord_dst_simple((tr1['proj_lat'].iloc[i],\n",
    "                                  tr1['proj_lon'].iloc[i]),\n",
    "                                 (tr2['proj_lat'].iloc[i],\n",
    "                                  tr2['proj_lon'].iloc[i])) \n",
    "        print(cdst)\n",
    "        if cdst <= cfl_dst:\n",
    "            ttc = tr1['ts'].iloc[i] - tr1['ts'].iloc[0]\n",
    "            \n",
    "            return ttc\n",
    "    \n",
    "    return None\n",
    "\n",
    "\n",
    "def ttc_est(lat_1, lon_1, lat_2, lon_2, hdg_1, hdg_2, spd_1, spd_2):\n",
    "    \n",
    "    knots_to_ms = 0.514444\n",
    "    ipz_lim = 9260 #meters\n",
    "    \n",
    "    alpha_1 = heading_diff(calc_compass_bearing((lat_1, lon_1),(lat_2, lon_2)), hdg_1)\n",
    "    alpha_2 = heading_diff(calc_compass_bearing((lat_2, lon_2),(lat_1, lon_1)), hdg_2)\n",
    "    \n",
    "    gamma = 180 - (alpha_1 + alpha_2)\n",
    "    \n",
    "    if gamma < 0:\n",
    "        return np.nan\n",
    "    else:\n",
    "        dy_1 = math.cos(math.radians(heading_diff(hdg_1,0))) * spd_1 * knots_to_ms\n",
    "        dy_2 = math.cos(math.radians(heading_diff(hdg_2,0))) * spd_2 * knots_to_ms\n",
    "        dx_1 = math.sin(math.radians(heading_diff(hdg_1,0))) * spd_1 * knots_to_ms\n",
    "        dx_2 = math.sin(math.radians(heading_diff(hdg_2,0))) * spd_2 * knots_to_ms\n",
    "        \n",
    "        dx = abs(dx_1 - dx_2)\n",
    "        dy = abs(dy_1 - dy_2)\n",
    "        \n",
    "        s = calc_coord_dst((lat_1, lon_1),(lat_2, lon_2))\n",
    "        ttc = (s - ipz_lim) / np.sqrt((dy**2 + dx**2))\n",
    "        \n",
    "        return ttc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n",
      "Error in realigning Conflict\n"
     ]
    },
    {
     "data": {
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oHu94dFnWZumTyydf5hff/t+oqjKn/eVshlh44a1my0XXpeMochan2zerMHD7aslmUrz0xN+hqQp3PPobWKz2JZrp8mC2WNl0y+088uk/4c5HP4vD5eXEi//BT//pzzj/5jOFDP+VhGHOWuEIa7x2VrHw4nhNxGlxrMkQ33CgH0XOoMgyJtvsZo4jv/g3Bm9KvP93/mrZ7+BVVeXCkefYabNjtc+eJ+Gu0gMp4uER7vzl35yUS1LJCIKJ1i27adm8i+Hea1w69jzn3nyGiyuwha8hRFYLa1MRKYT3Nk0yZzkJpsLLMaVlJRrSc2ZUJQvMfFceDPTSLZ0A9BpTyx3N1Nt5mujoIN72WpiDnb+msR13VS2bd99B6+ZdSzDDlYcgCPjbNuNv20xouA/p2It0nn6NzlOvsW7bfrbd+m6q/S3LOkdDiKwS1DUqRQYSAdxWFx7b+Kxll8VFrzywTLNaHjRNI5YTIoqcnXX/c4d/Vvg7GhxaViGiaRrn33oWb00DNpKYXNWzHuOuquW9n/3zRZ/baqG6voVDD3+CXXe8B+n4S1w7e5ibF4/RvGEH2w7ej79ty7Jom4ZPZJWgaersO1Ugg4mhSaYsyJuz1pZPJJ2MkU3r71mR5Rn3Hem/Sd/Vc2y85TagqMEsF33XzhEe7mP7oQfR4qMI7pplnc9qxuWtYd+9H+S9n/0Ldt3xHkYHu3jpib/j+e/+P/RcOY2qLu1aYQiRFU7+zmKtOtYH4+PDe/O4LE5SSgp1DQnX2BhBoCgzayLnDv8Mu9PN3ns+gMlsJhZcPiGiaRoX3v4Fbl8d6zbthEzSECJlwO50s+PQQ7z3s3/Ogft/hXQyxhtP/TO/+PbfLGk0l2HOWiDKaA+mKj+CZXGjRtQ1WPcknk0QzcZodE/WRMZmrbutldOedCaiYwSBOoMmMtTTycDNS+y5+/3Y7E68NY2EAr1LMcUpGey6zOjATW5990cQUhEATO6V4RSuBCxWG1v23MWmXXfQc+UU4eE+TEtYDma+nQ2/BHwI2ADskiTpXG77VuCbQB0wAnxKkqQrizW2UlDjQRL/8QWsO+7DceenFvW11qImMlgovDhZExmbtb5WhMhcNBFN0zj3xs9wuKvYsudOAOpbNtJ16TiqqmIyLb3x4cLbz+L0+Niw4x2og/pP2NBEyo/JZGKduB/E/Uv7uvPc/8fA3cDNCdu/CvyDJElbgX8AHl/ksRWB0i8BoMWDi/Ya+RDftWS2yVMsvDiFJlLoKbJ2/CJjNZHpfCKDNy8R6L3KjkMPYrHaAF2IZDMpIiNLH4gQ6LlKoKeTbbe+G7PFWvitmAwhUjHMtz3u6wC5Purk/m4A9gMP5DZ9F/h7URT96LlyZR2TJGl5PYRjUAY7ATDVb1i8F8kFW2hr0Jw1mAhgEczUOSYvOK5CT5E1JERCAexON+lkPBfiOx5N0zh7+GlcVbVsuuX2wva6Zr1N63D/9SUPB73w9rPYXV427dLno+aEiKGJVA7l0G3bgV5JkhSA3GNfbvtijK0Y1GDOzrwELVrXqjnL76rHPEVOgXONlYPXNI1YMICvXhcCU4X4puIRRgdu0rH3bsyW4nfSU12P3enh+PP/TiIaWrI5jwzcZODmJcQD7ypoRVp8FOzuRfchGiwdFe1Yr6vzlHys3z9729VeZBTA47ZRPYf9S8Fu0xcDr9c+pzmVm+V4zTzD6WHW1bRMOQeTWzfnmB3aks1xOa9FPBpGzqZpal/PUPcVXE7LpPkMpnQlvW39uklj7/rAp/jFvz/OqZf+nUd//Q8RFuAbmet1OPLzF7E73dx2/8PY7Lr5cUCOQlXdsl7LclEJ76EclEOIdAOtoiiaJUlSRFE0Ay257cIijM2ZkZEYqjr/O3i/30sgEJ11v2w6DUAsniI7h/1LIZPRF8twJDmnOZWTuV6HxUBRFQZiAXbX7pxyDmlF9xENBYNLMsflvBYAgZ7rANjc9QB0nj9NXfv4LO6+nn4AMqpt0lx9TdvY/67HOPb8v3P4uafZuv+ekuYx1+sQCvRy/cJJbrn9EcIRGdCPSY0GEJy+Zb2W5WC5vw+LhckkzPvme8HmLEmShoBTwMdymz4GnJQkKbAYYwudb1mZJVa/HOTzT9eaYz2QHEHV1CnDewFsJismwbRmzFnRkB5k4G/ZhCCYGOm/MclPlozppiqnZ+ps8E273knLpp2cee1JwiP9izrfC0eew2Kz07FvvLDS4kFMLsMfUknMS4iIovhlURR7gDbgeVEUz+eGPgf8viiKl4Hfzz1nEcdWBFpeiCyquyKXbLjGHOvFwotTtw0VBEGv5LtGijDGggEEkwlPjZ/99z1GNDhEeHi8IEjGwggmEw7X1HeSgiBw8IGPYbHZeevpf0VRZs56L5XI6CDd0kk69t6NzVEMv9ZUGS0ZMZzqFcZ8o7M+D3x+iu2XgEPTHFP2sRXDHOoXlYu15lgfjE+fI5LHZXWumZ4i0VAAj68ek8lMW8ceTrz4BN2XT46LtkpEQ7kS69PfGzrcVRx88GO8/pOvcf7w0+y+69Gyz/XikecwWyxs3X/vuO1aIgxohhCpMIyyJwugoIkswQK/1jSRgcQQ1XYfDsv0JcOda6inSDQYwFOtC1SHy0tDewdd0olx34tkLDynIoutm3ex6ZbbuXj0BQI9V8s6z1h4hJsXj7F59x04XOMdz1pM71JoZKtXFoYQWQhL4RPJOUVU1pZPZDARoGEGLQRyjanWQBFGvXrvMN6a4vVo37qfWCgwrpxJMhaa1h8ykb33fhCPr463fv6tQlHHcnDp6PMIJgHxwH2TxtSEkSNSiRhCZCEUfCKLqSWsPZ+IpmkMJoamLLw4FpfFuSYy1pOxMIqcKWgiAG0duxEEE92XT47bb67l3q02O4ce+STJaJATL/2gLPNMRINcP/8WG3fehss7WZhpMSNbvRIxhEiJaEtcbnkt9ROJZGIk5dSU5U7G4rSuDXNWvmbWWE3E7vTQuG5rwaSVTSeRs2lcc9REQC+Hsv0dD3LjwhG6L59a8DwvHXsRTdXYfvDdU46riaCemGt3TzlusDoxhEipLIEpC4q1s9aSJlKIzHLPwZwlJyv+2uRrZnmrx1+PdnEf8fAIwaFukjG9y6NzCg1gJnbe9jA1jes49vz3CucohVQ8wrWzh1m/4yBuX92U+2ixUQR37bK36TUoL4YQKZUlEiJ51lJ0Vl6ITGyJOxGXxYmqqaSVpeudsBzEQgFMZjNO73gzUOvm3QgmE93SSRKFHJH5dS80mc3c9sgnUeQsR37xbyULZOnEyyiyzI53PDDtPloihMk9PyFnsPIxhEiJaPLYhWvxFvi1mGw4GA9gM9vw2atm3G+tVPKNBvPhveN/rnanm6Z12+i+fLJQE8tVQgvcqtpG9tz9fgZuXKTz9GvzPj6djOs9v8V9eGumF/xqXNdEDCoLQ4iUylJpIsLaM2cNJIZodPkxzZDvALpPBCq/CGM0FMBTM7Vpr13cRzwySu/VMwA43KX1Ud+y506aN+zg9Cs/ITI6OK9jr5x8BTmbZvs7Hpx2H01T0eIhw6legRhCpES0sSaUJVjf15Y5KzBjkmGevCZSyWG+mqYSDw3jrZ76Dr918y5MJjN9V89jc7gK1XLniyAIHHzwY5itNt56Zu7Z7Nl0kssnX6F18+4Zy8xrqRioshHeW4EYQqRUxmWrG8mG5SKjZBhNBWf1h4CesQ6VrYkkoiEUJTutJmJzuGjasA3Q5pwjMh1Oj4+DD3yU4GA3F976+ZyO6Tz9Otl0kh2HptdCoNi4zRAilYchREpEW7LoLJ2F9ljXNI3emyGyGWXhk1pEBhPDANMWXhxLQROpYCFSCO+tnl4za9+qt0OdT3jvdLR17GHDzkNcPPIcw33XZtxXzqaRjr9I04bt1Datm3FfLW5kq1cqhhAplbFCZCmSDReYsT7YF+HJ757m/Mm+ckxq0Zit8OJY1oJjvRDeO40mAtC6+RZMZguuqvLc5e+/94O4vDW89cy3yGamr0129exh0sk4Ow49NOs5jY6GlYshREpkfHTWErzeAgVVMq4Lvf7u0nMBloLB+BACAg3O+ln3dVgcCAgV7ROJhgKYzdYZQ3etdif3PvZ7s5qU5orV7uTQI58kERnl5Es/nHIfRc4iHX2RhrYt+Fs3zXpOLR4EwYTgLM3xb7ByMYRIqSxZnoiW+7882s5K96wMJgLUOWqwzqHlsEkw4bDYK9ucFQzgqa6fsTIvgL91Ey5v+e7y/a2b2Xbw3Vw//xY9nWcmjV8//zbJeJgdt82uhYCuiQgu34I6KhqsTIxPtFSWSBPJayALdqznnSsr3EE/kBiakz8kT6XXz5opvHex2Xn7I9Q0tHHsue+SjEcK21VF4eLR56lr3kBD+9Y5nUuLBw1TVoViCJESGe9YX7yFOX/mhdbOWg2FJlRNZSgxPCd/SJ5KruSrqirx8PCMTvXFxGy2cOiRTyJnMhwdk81+89IxEpFRdhx6aM4lTIyOhpVLOXqsI4riBuDHYzZVA1WSJNWKongDSOX+AfyxJEnP5o67DXgccAI3gE/kWuPOOLYiWKKGVHmH+sI1kbyDfuUSTIXJqtk5hffmqeSeIoloEFVRlk0TAfDVNbP7rkc5+fIPuHrmDerve4iLR56j2t9K88Ydcz6PGh/F3LZzEWdqsFyURYhIknQD2Jt/Lori304492OSJJ0be4woiibg28CvSZL0uiiKXwC+CHxmprFyzLccaEsUnZUXHgvtJ1K4X1zBUqRYeHEe5iyrk8FEYLGmtKzEgvr1WC5NJE/Hvrvou36OU6/8CIuQJRoc4p3v/czctZBMErIpBEMTqUjKbs4SRdEGfBz4l1l2PQCkJEl6Pff8q8CH5zC2MlAy6Evz4hqK8mv+QjWRfJ7JSs58zwsDw5ylEy2UgJ+7UF0MBMHEOx78OGazlbef/xFVtY20deye8/H58F6TxxAilchi+EQeBXolSToxZtt3RFE8I4riV0RRzGdErQNu5neQJGkYMImiWDvL2IpAk7N6b4RFdjaUy7FeqN+4cmUIA4kh3BYXHuvc+01UsjkrGgxgsdpwuGcuRLkUuLzV3PrARxAEgZ23PzJrtNhYCtnqhiZSkZTFnDWBzzBeC7lLkqRuURTtwN8Cfw98YhFedxJ1dZ6Sj/X7vTOOD9tAsdpQ0zIul43aWfYvFatN/7HanZZZ5zQTgb4oADbb/M6zkNecL8Fzo7T5mmhomPui6Q9Uk+3OUl3rmFNY8EJYymsBkEkEqa5vnNf1WEz8/rvZsf8ADuf8mkpF+xIkgfp17VhrlvYaLiZL/X1YqZRViIii2ArcA3wyv02SpO7cY1oUxa8AT+aGuoD1Y46tB1RJkkZFUZx2bD7zGRmJoarzv/X2+70EAtEZ90nF4mgm/fIl4mmUWfYvlXRaL4SXSGRmndNMhMP63XomI8/5PHO5DuWkO9TPzrpt83pNLa0L2a6BAFW2xftRL/W1ABgZ7Kfa37rkrzsTpVyH9EA/AMGUFWEFvZeFsBzfh6XAZBLmffNdbnPWp4GfSZI0AiCKolsURV/ubwH4KJDvw3kccIqieGfu+eeAJ+YwtiIomLMW3SdSHl9G3hq2Ugs5JrJJIpnovPwhoJuz8sdXEqqqEI+MzFjuZLWgxYMIdg+CpbQKwwYrm3ILkV9jvCmrEXhZFMUzwDlgK/C7AJIkqegayz+KongFXYP5L7ONrRiULIJl6XwiC21Kpal538qCp7QolOJUh8qt5BsPj6KpKp5ljswqB6qRaFjRlNWcJUnS1gnPrwH7Ztj/MLBrvmMrAU3Jgjl/Z7WYyYblcawvtAowQEpOYxIEbOby31GWEt4LlVuEsVC9t1I0EUOIVCxGxnqpyBmE1WTOKsE3NJH/69U/5Qtv/NWCzzMVg4kAZsFMvWN+AXjTmbOyqkw4vXpt1oXqvRWgiWiJoNHRsIJZjOisNYGmZBEs9tyTxUw2zD+WyyeysPnE5cTCTjANg/Eh/M46zCbzvI7Lm7O6oj2klTRd0R66or30xQZQNIW/uP2/UO9cMZHhcyYaCmCx2bG7VncEkKZk0ZIRo7d6BWMIkVKRs2B3l1URUVUNk2n8CcvnWC+kLS7weAino/hUxHcQAAAgAElEQVTs5V3cBhIBmuZpygLdnCUg8GL3a4Xn67xt7KrfzqnAOcLpyKoUIrHgEN5q/5yzwlcqWiIEgOBeeMMsg5WJIURKRcmWNdqkNxDji985wWfes519HUUTRn7xvhy8OuVxVyMJzo5G+aV2Pzbz9NbJghAoURaN9Tn0xPrw2cXSTjQFiqoQSA6zxz//2koWk4Xf3PUpFE1hnbeNOkcNgiBwJXiNU4FzZNWlKtlfXqKhALVN62ffcYVTyFY3NJGKxfCJlIimZMoW4puVVb721AXiKZnB0YkOYn3VTylTd5j7+uVejgQidMfHj2uaRuDav5MIXtCfq/ntpc0xkin6F3qivaWdZBqGkyOomjqvwotj2ePfyf6G3dQ7awt37rZc4uFqFCKKIpOIjFaGP8ToaFjxGEKkVOQsQi5KaaH+ih+9do2uoRgAmez4Huj5EvApOT1lmG/+pSMZefxxSpJkWCLY95z+fIG1s8YKke5YeVvsDuTDe93lWzStJl2IZJasedjCOT4cIZzJEg+PoGnaslbvLRfF3uqGEKlUDCFSIpqSAcvCNZGLN4M8+3YX9+5twWI2kZogRLQxi39GmdwIy2PVHdGRrDzhOP08SkZvh7vQ6KxILtKpydVAb7S8QmQ+fdXnSl6IrBZNJKOo/OD6IP94obsY3lsBmogaD4HFBjbXck/FYJEwhEipKNlciC+U6miIp7L8008v0FDj5CP3dWC3mkhPECJjz52UJ5u0LDlHfHiCJoI2tTAqlbwmsqNOZCg5TGqKuZTKYDyAz+YthOuWA6tZd/etFiEi5z6fSFYphvdWiCYiuGtXfYCAwfQYQqQENE0FRV5wFd9v/+IykXiG33p0J3abGbvNTCYz/eIfzyZ45voLPHH5J4VFPT88UYho6sTz5P8oba6RTAyLYGZrzWYAemL9pZ1oCgYTQzSW6A+ZDlteE1kl5ix5jKYYDQWw2p3YHPMrdLgSUeNGjkilYwiRUlByC/YCorOu9oV5+8Ig77tjAxub9Sqtdqt5kiaiomEWdJPVxdHL/PT6s7zc8wYXRy7r4/k72EmaiDrhad4sVhqRTBSvzUu7txWAnjKZtDRNYyARmHem+mwUfCKqPMueKwN5zM1CLBioiPBeMLLV1wKGECkFWfdNFDLWpzEVPXe0m3/88bkpx05dGcYkCLz7QFthm81qJp2d7Dx3WhwASMHOwraMqs8hfwM7nU+k+Hxh2YaRTJQquxefrQqP1U1PmZzr0WyMpJwsqz8kq6o8fqkfm3XHqjFnZdXi5x4NBSrDqa6paPGQoYlUOIYQKYFCa1yLrdC7fCoudQXp7A1POXa6c4SONh8uR7EHxlSaiKZptHqacVtcXBy9XNieN9Pkl55YVkEZYxKZKETUhckQXYjYvAiCQJunpWxhvoNx3aleanjvVHSGE/Ql0jgdd5CRV4cQySj6ByOoMolIsCKc6loyAppiaCIVjiFESiG3gAuzNEGKJDLjFvY8o5EUPYEYu7fUjdt+uTvE5e7QuG0qGk6Lk/vW3V3YJiBwM9oD6ELGIghoQHSsNjLRnJWvBlxilFYkHS3062j3ttIXH0Qug6looeG9N6NJvnKhi74xeTIXQ/HC3xF5fmVUlot0ThOxJSNApYT35rPVDSFSyRhCpAS0/N2tZWYhEo1np1y0T18dAWDP5vpx22u89ileTEMQBN7ZcpBqu49DTQfYXreVS6NXAF0TqbbrkUhjneuTzFmFUvDzFyKKqhDLxgtCpM3bgqIpDOS0iIUwmBjCZrJSbfeVdPyFUIyeeJpvXukjmpVRNQ0pHGeT1wmaSjDjRtU0vn91gM7I4tT9Kgf5z86e0hdeb/Xy9lUvB2ohR8TIVq9kDCFSCspYnwjT2oim00TOdA5T73PQXDc+dv7Q9kasFtO4hV5DQwCqbF7+8o7/xqd2fIR2TysJOYmmaWiaRrVNn8dgsphHMp1PpBRNJJaNo6EVNRFPC1CepMPBeIBGlx/TPHp2j2UgkUEAUorKdzr76U+kiWYV9tVXITDCaLaeLxzr5NRolO9dLV9EWbn5wfVBAOzJnBCpCE3EyFZfCxhCpAQKPhGzjelifDNZhVRGmbRoZ7IKF28G2bOlflL0TZXbRlZWSY0J89WFyPj9XFYnqqbyau+byEqSapuuifz45hDfu9rPxWBsiuis/OP8hUg+nLgqV3TR76rHZrKWxS8yuIDILE3T6Euk2V9fxWMbG+mKpfiHC90AbPI6sRBEpagtJmR1xXZ2zGNPhrA53Ngcqz85T4sHQTAjOFZGj3iDxaFsBRhFUbwBpHL/AP5YkqRnRVG8DXgccAI3gE9IkjSUO6aksWUnF51VNGdNXpgiCX2fiZrIpa4gGVllz+a6ScdUufXzReIZnDkTlZYzZ40l34jp+5d/jCC4SFX/MqD3RT4zGuPMaIwvdEx0rJeuiRSESE4TMQkmWj0tdJcY5ntqJEIgmeXOJi+jqSC3NR8o6TxDqQxxWaHZZWdXrZeBZIaX+kZxmk3U2K04hR6ybOWdjdWcHY0SzSr8t2Od/Pn+zTMWq1wO1nsc3IylsCXDuKvrZz9gFaDGgwguH4JpZV1rg/JS7k/3MUmS9ub+PSuKogn4NvCfcl0PXwW+CFDq2Iqg4Fi3TZtsGE3o+0y88z3dOYLdakZcN7k0ts+t+0TC8bFmqcmayCbfehpdDTy66WEELLzR8z32VE+w90+ss5WPzppGiFy4cJbh4cCUY/mSJ3khAtDubaE31jfvtr3RrMz3rw3yUv8oP7rRh4Y2LtEwlpX5r0ev8M3Ls2s5AzlB3eDQ83Xub6nlN8RWPre9HQCHWaNOeIH3rvPz8S3NheNe6hstS6fHPOGMzOVwfPYdczzXM8Lx4ci4bUpuPvZkCJdv9ZuyIJet7jH8IZXOYt8iHABSkiS9nnv+VeDDCxxbdgqO9Rmis/KCQFG1Yv0rTePM1WF2bKjBapkcNVTl1hfDyFghApM0kSZ3I//9tj/iwfXvwuP+ABaTlf7Y2fFznBTiq417HEskEuLYsTcJBAanfC9FTcRT2NbmbSGlpBlJBqc8Jk9KUbgYivHmYIhnuof54qnrhbFzwSxgxu/ycywQ5kc3Bvm6pAsPKTy7EzyfW1GfC5M2CQKbq1z4nfp1tJqsyJp+LVtdDlpcupB+ZSDI/zp5jf5EetbXmApNVdCUYhDD35y+zjcu983JVKZoGi/1jxZ8IHkSsoqgZLFlYjh9laGJaPEgJpfRR6TSKXc/ke+IoigArwP/FVgH3MwPSpI0LIqiSRTF2lLHJEkanetk6uo8s+80DX7/9E2Xov1mUkBdQzUpkwmn00r9xP2vFadZV+/FbBK40R9hJJLmYw9tn/L8ltxiqAhCYdxkAqfDNuX+qqYhCDbWVW+jO3IZm+NWhFx2u8djZWTMe3HkzGMCwqRzSdJpBEFg//5deDzjr5nf7yXbncZpddDaVDTB7TZ38G+XIGIaZYd/w6S5aZrGkf4gT1zsIZzWF1xzLhT5sW2t2M0mvnO+G4ftABfjdl7pHsJjNVPvsuPMyiRlFVe1E7d1+q+oPa6XzW/ye/HaJwt0j9NJVI4U3u9fNO5E0zS+ebaLN3pG+LvzXRxsruGzezdgmkN2eP48gae/ihwcoPnjf66/19y4t8aN0zpzSHFKLgr32joP5lzts5SiUpXVKznXt7aO+4xSiRg2uxOTeWWEK8/028ijaRqxRAiXeOvk30aFMJfrsBYopxC5S5KkblEU7cDfAn8P/KiM5583IyOxknwAfr+XQGD6/tyZoG6KGA1nUFWNZCIzaf/ewaK5YnAwgtVi4uWjulzc2OCe8vyqqiEI0DsYLYzLiko6JU+5f0ZRUdIKLmUL8aFB0toN3C0bMNvMRMeEswYCURI5048sq+POpaoqZ8+eo6WljWRSI5ksjuWvw2B4BK/VM+44h+LFJJg433uVzY6OSXP7/tUBTo1GaXXZ+ZDYSKPThttiLizWsqphFjLY7Xt4pXuU/fVePrShEUEQuByO843LfXzx9Uv8utiKdxpB8tRlPdoqEkyQmsLHocqQzKQnXbtfaqrhjR5dxB7tDxKKp3n/hgZqphBEE6+Flk0RO/sK1o47Jp23ezA84zkUVWM4XdQyO3uD1DqsqJpGQlZoS+uJqbK5+P3LpJM8+fgXsDlcvPezf4Fpmf0Ls/028miZBFo2RVqY+ru+2pnrdVhtmEzCvG++yyZEJEnqzj2mRVH8CvAk8P8ChfZsoijWA6okSaOiKHaVMlau+S6IvE9khoz1SLyYKZ0XZKevjrC+0Tt1Pgj6B+h12YjEdTNLVlbIJu0EZRuvne5jJJJiJJJiNJLOPaaQFQ3dk7GPDEAmRNXWunHmLE3TCi4SWRmfIDgw0EciEefWW2+f9u3ms9XHYjVbaXI1TFn+RFE1zoxG2V/n5YMbGzEJAoGBKLLVTE0urNliEnCobxEX9CTK97QXa0Vt9bn5dEcL/3a1n69d6uHzO9dhmbB4Xg7HickKzU7btE5ym8k6ZdkTQRD4zW1txLMKcVnhme4A/+fsTWrsFn59ayvVMwmC/ksgZ7BsOjhpbCCRnlGIfP/6AGdHY4Xn37naz+/vXEdS1j8cd1q/8bBVFTW+yEg/ipzF7atbdgEyH9SYEd67VijLt1IURbcoir7c3wLwUeAUcBxwiqJ4Z27XzwFP5P4udWzZ0fJ9PWYoBR9NFO84FVUjlsxytTfM7imissZS5bLx6ul+/uDLr/HbX3qFwLF9nHqziq8/c4mn3rjBhRtBMrLChiYv79rfhndrNffft4nf/JV2TN4RUsP56r5jhYhc8IWkJ5QBuXr1Mjabnfb2ddPOaSohAnrm+lRhvuGMjAps8DoLmsfLz1zmtV9cKeyjairDiWtscnfz29vacE7wEYnVbn51czPDqSxvDU0uHfPTLl10fnhz07jt8kgCTdEXZavZOm0Bxo1eJ7fUejjU4OND9NJ0/Q0CqSx/c+bGuMz/lJzimxe+x1Bc11zUhD4Xk69x0jkn1i8bi6ppnBuNIQDvW+fHYzHTn0jTGUlwLaprjfZkiKzViWAt3mSEhwcAuO3hT0577pWIljCEyFqhXJpII/ADURTNgBm4APyuJEmqKIqfBB4XRdFBLlQXoNSxFcFEx/oUFrOxEVaqpnH22giaBnu2zOw0vWdvC6c7h6mtclBXZeeFgWfpaGziw7c8RI3XjmXMXXdKVjh78hrr233c3lTDN2tfI3OzDjkpj3esq0rB6StoAqF0mGq7j0wmQ1fXdbZsETGbp/8qRNIxqmonC5E2bwtvDxyfJGRG0/r1qR1zV55Jy0RCqULIcigdJqNmEX0u1nvH9xH53tV+dlR72F3npaPKxdPdw+yrq8JtNZNRVP769HWSisrtDdU0OosLbqYrTOKlG1g3VuO+ez3WaTSRiVx45QfUAf0t+1BsTn5yY4iPb2lGEASODJzgyMAJHss8jBsbWs7cJzgmX4+8RjEVI6ms7g/a2Mj+ej1v4qmuAP8iFYWwJREk4aweFxYeHunHYrXjqprbYtzVdR2bzU5TU8uc9l8stJiRrb5WKIsQkSTpGrBvmrHDwK5yji07ShbMFgRBmBR+m2esJqKqGmeujlDlsrKheWZn3P0H2rh/TGXfN14forbej796csOm/JKVn4HbbydzEyJ9hznsDbMzt13T5DFJ9QIXRiTe2fIObty4iqIobN68ddr5ZJQMKSWFdwpNpC2fuR7tY2edWNg+lRDJZhUyaZloOE1VtYPBuK5JNE2o3qvl7tidZjO767zc31rLlUiC53tH+OUNDYymsyRzmsahhvGlUlKn9Lv27PUQ2Y46PTpLlVE1ddqMeHVM35X76+3greMXvSOcGomyt87L631v0+5pYX11G8PDMb2ooNWhmzJzmAUBRdNIKhMbihXJ+0L8uXDkvIZmMwl8bLMefnz+eJC0t60Q7gsQHu6nqq4JYQ4Z/fF4jFdffYFt225ZdiGiFjQRIzqr0lk9RtYVhKZkJ4T3TpVsmC24SzKyytmrI+zaXDenKKDCWTWNaCY2rd8lb6LKn/OTex/C5JRRgjWMJkcK+w0nhkhm9EgmQRO4Gr4BwMj519llGaW2dnrtKJLRbfhTmbPyQmRiu9zRdBazAFW24j1KNpeFP5LrJT+Qb4k7IVs9raioQDbnxGl362XwL0finBmJ8uXzXQDsrHHjH1MBWYmmUYMpHPuaMHltJN/sxq7p4zMVisymk4W/tzsF7m6uYb3HwVNdAS6M3qA31s87Ww4V/DVaKjpOC1E1rbDoJyZoIrFs0Yw4nNIFa11uzhu8DjZ6nTzQWodY7WaTy0I2ESXt8I3rLRIe6cdX18xcOHPmBADbtt0yp/0XEy0WRHB4Zy1SarD6MYRIKchZPdEQplzgVVUjmsjgy+V9XO+LkEjLkwouzkZfXL+znmoBh6Loyk9hb309Oze1oESr2VW7u7Dfl08+zoVcEysBgXAqQuTGWbYF3mRL7BLKtSPTzmGqHJE8LquTOkct3bHxfpG4rOCxWArCTdM05FyflMCgLkQGEwGcFide6/jzJnJaRjZn0skv3sG0zI9vFgsWfGRT87j8mWyX7pS2bqjGeXs7ajTDui69M2BmBpNWPFKM1UgnY5gEgcc2NpJSVL5x8WWsJisHm/YW9tFSUQRn8fPIjOkDkpAVUjltpCee4q9OXedELqlwOJXBZTHjyvl+Gp12fnNbG3c06WaqfF/1tLO60OUwlYiSTkTx1c8uRCKRMJ2dElu3bp8Upr0cqAmjGdVaodx5ImsCTcnMWME3lsqiaVDtsROKZbhwcxSzSWDnxvnZh1vcTfzWrk8VWtJOJG86N40xqW1cV83Z80OQKdZesjDe7JaNBcmcepwzmfXEHM1EnznJHe9bz4YNkxeriXWzJtLubZnU5TCtqOMipuQxjbaG80IkPkSTa3L3vmQuj2Jsu9hPd7TwzSt9qJrGRzY10eFzFXrLF16jJ4yp2oG5yo65yo6to5aGzhGaG2r03ivTfFyxYDFLP53Q51bnsPHL66r51/Od1Lk6xvV+15LRcSaawTFmy4uhOP/jxDU2eZ1cj+oazg9vDFFls9AbT1M/Q+TWWCGSF6DhYT2EeS5C5PTp45hMJnbtmtKqvOTke6sbVD6GECkFZYwmMgXRnFO92mMHoly8GWRre3WhHtZcEQSBPf7pTRNawZxV3La+2YtgFugejlKduxE0C4BW3Ond167zUmQLzyT3QgwsKDQdfp716z8xaVGfquTJWNo8rZwKnCMlp3DkOjCmFRXHGCGSHdNoa2Qoxo3OETjciv/21KTzJXJCJDtGiGz1ufhkRzOtLsc4E1keNS0jD8ax39LAwD9/DbPPR937Pkji5ggfGD1EVs5MOiZPNFQUIqlEMe5flq8BWTLCLXTFkoXEMi0VwVRXjGRL5DQPn9VCOBeddS0nQGpsFmKywjcu60L2AxumLzQZzQmzjNNXyMQPj+SEyCzmrGBwlOvXO9m5cw9O59IUblQ1jb54mp5Eip54ivtb6saFN2vxEOaGqW9+DCoLQ4iUgCZnij4Rk7nQc/3atSsEAoNUNe4AoDqXDzIUTHLfvtayz6PgWB+z8G+qduOqdxKNpiAvRACLUPyo/UmVr6R2srnJwR989CDmi8+RPfoC8tUOrFtuG/cakUwUAWGS2SlPm1df4Hpi/Wyp3gjo2df2cZqIvtDW1LsIDie4eKYPW8yL6YoHDkAqmWWgJ8y6zXWFCKex7WIFQWB79fQmGrknAhpY231EvvKG/h4f+zAjOwRaT9WRliJwYOoFPBocwuWtQZGzpJPFHI43+o7gd3WQMfn47tUBDmxsQFNVtGQEk6tYlTaRe29uq5lwVmZHtZsLuaZYf7BrPf2JNN+/NkiLy86t9dNXs42GAtjdVahm2zhNxOZw4XDPXAX31KljWK1Wbrllz4z7zUahXQC6nVsQBFRNI6OqpBWNtKJyrW+UY90jXA4nCgIfdG34gxv1sGdNzuhmP0MTWRMYQqQUFLlgzhI8tahR/S7y9ddfAkD06hncNZ6itrJ7ltDeUihoImO2+WwWdmyohVBxEf6l9fdxva+OwaC+SD6p7iKtWfnAPVtxO6xoex5GuXmM9BvfxtyyHZOrGPUUyURxW12YTVOX3NjoW4+AwONnvsHv7vkMKTlNVwx21hQX/bxTvbndR3A4Qfd1PXIndkNgeDDGj759EjmrcteDHSTa9DtpeR6VBrLdEQSnBVNtMdxXOvICly8fYcS5i+0X21E60pirJid5RoMBPNX+nP9Bvz7d0T5uRrvZ1/wxrsX0vJcnL/fxDlMSVAWhqiiQ4jmht8nrLJSlvxiKI/rcWE0m1nmc/NHuDbO+h1huHlDUwiIjA/jqmidph2MZHh6iu/sGe/feit3uIBaL8vbbrzM6OoLNZuPRR3+FuKzQn0gTysjEswomAWxmExlFJZpViGUVYrJMIJwioSjIFhN2kwkVbZxGmMdlMbPV52Krz8V6j5NX+oMcH47wQFsdXqsFLaH3RFltvdVlWcVsFma83gaTMYRIKciZQtSJqaoRZeDyuOF8GXjdnAW1VXaaastvZsj/vid+55tbvGTCxR//ttpNdGd0p7SGxun4ZmrdKts36ImPgsmE457Pkvjhfyf9+r/ieOD3Cj+k6RIN83isbh7ecD/P3HieLx3/BwBs1l1YTQ8U9snmfCLNrT4unOxHyWokPEF82Tpe+Omlgs8kncoWIpyyc6yyqykq2d4otk3VKOFiUuJA51kSQ/28uSPJtpF2km/24H5w06QFIhYK0L51H2gqqVwOyOG+t7GaLMSVGiCL12rmqc4BNEuAXYBpjBBJyApmQeChtno6fC46fG7+ZO/GcZrYXIiGArRs0oOys6re9yQ80s/6bTOXyT9x8hiKtxZL62Ze7bzOWUlCNltxtu9Etrv469PXiWSnDz22mgS8VgseixktlMaZlNm3v5WUomIzCdjNJuxmEzaT/ri5yYcro4yLMryzqZqjgTBvDYZ5oK0OdYU2o1IUlXAwyWggXvgXjaSxOyx6uaGbIR7+4E42dFRGAcylwhAiJaApWQS7LhRMvibkzrd0E1eOcCyFSRBw5Oz3Ha2ltX6dDZW8JjJ+YXS6rKi24iKmxkdRRnqARkJAUrGxb7t53IJqrmnBduADZI48gXztCNbNhwCIZmIFITK2t4mmafTeDGGzm3nvpgeRVZkroWvUOGo4OXSaSLIR0LPJ8+YsT5UdT5WdWCRNyhXhXTv28fbLNwpz6LwYwOnXBe9cNJGMovLEhR5aPQJ3tPuQR4uVceM5X0dWTXBjY4xNVyDTGcTeUTSxpJNxMqkEnmo/mVSCUKCXtJLhyMBJdtXv42pSj+pyW8x01Hm5ef6MLkQ8xaoDCVnBbTFhNgl0+PRoMM8MRSOnIptOkk5E8Vb7MQv6e0/GQmTTSXz1zSiqRiQrE8rIhDNZwhmZcEZmKBqj29VKttrFlWu5yLWcH8JhNlFltbDJaafFbafFZafWbsVlMaOh+63yAgJgqD/CD568hiDAw788vVnMXz25Fla9w8b2ajdvDYW4p7kGYQFCRJGzBHqvER0dZPOeO0su9ZJKZhnsjTASiDM6HGd0KE5wNIGq5KP+wFfrosrnIJORyaQV9h5qp23DyhJ8qwFDiJSCMkYT8TUAGmqk6KCNxZN43VZ6h3XzyKZFEiLaNJqIy2Im7Syan9KnnkLW9KidAVRs5iyyf3LuhG33w8jXj3P+6HdoqmnA799NJBOlobqevq4QT//HObbubOTuhzq4eGaAV565jNNt5dd+/528f8svAZDIZjk7GubU0PP8368exmwys0+9DbBhtZkLQsTmMbH71nbOn+jHZBKIhFKMBuJYX++GW6rH+USmYyiZ4XwqzZVGG/c0e0geu6BfFyCR0v0SzYKPN52X6Gi4h9SxPqxtXkxO/bPLR0R5a/zEI6OkEzFODJ0hpaTYWrufq70qPpuFaFYhlMzQlNXDdU8mTBzIuSnislII2y2VaGEeDVijJjKqxmign5viA3TKTSSOd07KRHKYTZjSCZxyinY5Tma4jw3Nzdy29wBVDtukWmMTmagpdV7U56Bpeoi6yTQ/k86dTTVcCMU5MRJh/zx6q2uaRiwUYODGJQZuXmSo+wpyNoPN4WbTrtv1MtbzIDiS4MzRHqSzAyg5geGpslNb76Z9Uy21fjd1fjfVdS4sFiPDoRwYQqQENLmYbGiq0p2JcnigMB6Np6ly2ejKJda11bsXZR75ZXZiAmO900rUMUYTSQRJWv267Ruorx4kqkx2VAsmM457f4PvHv0/tJz8Ots7vkQkHcFnq+LMsR6yGYXzJ/u4+6EOoiE9sioZz6LIKubcDzKjgstxHxtdV/BYMvTHB7lw8RqNbOOZgWeRo7kMdUeWb0nfo39bkAa7H17Tr6Nq0xfkqWzxE4nloqEyZgHBbEIe1RMsbdu3oaBHRDULPo5FbuK4vZX4U1dIHunFfc8GoBgR5a32ExrqIZNO8Ebv2zS6/AimWmCYW2o8vDEY4lo4QUcmQsLi4kfdQWSTlUMNvkmRaKWQDzP2VPuxxlPImsrrIwlCDdvY6rbT5nVTbbNQZbPgs1motlkZHujhhRfexGq1oSgK9xy6gy1bxJLs+ZqmcfVS8SZIVVRM0/jApqPdZaPFYeHUSJQ9sVFki4N0NoucTCBn0yjZrP4o649yNs3oYDcDNy4SD+ufm8dXz4Yd76Bpw3Ya2jswjwmjV2SVbFYhm1EKj3LuMRxKERiIEhiIERpJYDYLiLua6NjZQJ3fg91hLHOLiXF1S0HJIljymkhOiASLQiSeSlPlchPPmUMWy1E3lWMd9NIaXbbia1o2H0LuNjEoZDFroDUM0RvP8hfHr/K5HW2F+lOZtMxLr4RIOh10kuTU8Z8gawovvhFlSzRSSLXIpOWCszHctkkAACAASURBVBwgEkpSkxOUgVQGQTCzv/FubvX7UDWVZ/pO0z0U4njoBFvi92LBzqDWTyik0VLXxKXwedxtozT1bCeeCqAoMrIw+13sxTHl9ntuBDGNhhEsFrT1bdClC5EqxUaWavpNozTsaSR1coDsxjDWdT5ioQCCIOD21WF3eYk7BK5HbvKBLe9hIJHGYzHT4XPxxmCIfY3VtHcniVm9bKpy8eTNIW7GkoQz8rjyLqWQ10Q81fVY+nsJpWU6tSrqRzv5tYOPTNpfVVXefPM1AGw2G/fe+yB1daXb8Qd7I8QiaeobPQwPxlAUDQ2Ft166xp53tFE1puSOpmnEwiMEB7uIR4JoqkJkdJDeq2fxp5OogpknNQWwweNfmPF1LVY7Des6EA/cR/OGbYXAAoBYJM2VC/3094QZ7I0QDiZnOBO4vTb8jV627WpE3NWEyz11CH4mLdN7M4Svxkmtf3Fu7tYahhApAb3sif4lFexuBLsHOVzMpk4k07TX1NAT0E0qyhydxPOl6FgfL6Rq7FaqM2EUK5jNILRsIyYnGdWgGYGUzYqJJGlVpTOcKAiRS2cGuHZpGH/jFgbWX+DFc8+Ay0J8xE00m6E253vp7QqRyRTNYaHRohDpiukaSrtHzxkxCSaElIW6miq+eOef8o239Oz4B8U7eXCbXgY+o2Q43nGGE08NocVlqs5fIlof49n6bm5t3Eudc2o79XC0mGfyw5+cZ9fNG6xzu1FqvNAFJpOJdCyLp+F9PHEjyGc7tmK6ESLxdi9VTR6iwQAubw1mixWHy8tAnRmzYOa2plv5+uVhml12grk6YHVOK+5MhF5rFY+u8/Ptq/2cGtF9AzaTMM5fNB3RcIoXf3qJex7ZSvWYQItoMIDTU43FasOMwI1YErucYHfi5qRzZLNZnn/+aRKJODU1tTz44Puw24tRZ2lF5UggTEpWC2ZOgbzJUyjccAhCvuaawMDxXgSTgKXVA4Mx3uoPMtg3Qm/vTSJn+rAqMTzZKKngIOGRPjKp8Qu61e6kdfMuIlYPl0ZC7Etcp8qs4tz1AKqiEA0FqPa3UlXTgNli09+n1YbT48NsthCPphkJxLne2UtoNEFfd5jR3G/H6bLS0FLF1lsasdstWKwmrDYzVqsZq82MxWrG47Xj8kyft5VOydzoHOHapQDd10cLZq5tu5t4532bsDuM0iwLwRAipTA2TwQQfI1kowFAz2ROpzN4XTZS6WHW14TR5AhQ/ph5jcnJhgAEe2iLd5Ou8oBZYzQcp0/WENBowEQPTqqscaxWC93x4kJcqB4r6I+dVgGHaiaZcRL7/9l78yA5rvvO8/Pyzqyrq6vvCw2ggQZBnAQJ3hJPUdRpjWSPPQ6t7d3weOw5bO/MeHdmY2bP2dgYT+yEbMvhQ7bH1u6MrPsgRVIUCVK8QIIgiLu7gW70hb67687K++0fVWgAvCQqqJG95i8iI7NeZmVlVWW+73u/3/f3/SE3QaRa9giDGCdt4NYCSteMEr34Ssna5kMtpaRS8sjmbWzNxkwr+LWEkZ7NcjEYqsHtvTfj9p5n4uwKtpsjWwz5duYxvj31GFuzW9iT3Q2Tbdx9925SLdab9COEIZACopSGHvuo2RSuaF5DW7qDcr1VQMzX+L3zc/y9/R30PztP/cg0lcUFUnY7iRuiWDbLeZWd9gCW5rDiBezIpXhhuUlX1RQF0ytTzfaAgN/aswU/Tvh3J6ZYbAR8Yfwyn9jSeZ2q8Bvt+e9fZGGuzNJ85ToQqZVWyeSbo3A3igkTydbxJylsvT5Zr1wuceTI96hUSliWxUc+8inUa6odTlZcvn5pmWIQIaRE86socYRdX8OuraBGPrLlphJJjJAxShJjr7lYFpQmIas1mP/2o2ixRx9Qe7V57pJh017oZuf+2zDTndheQm7LCFahA01v/tdLrs8PXp8gmF6mETeYP/EDqsXm4Gr00H1s33sH8fIFFKdtk0Y+M7nO4187u1lzRzdUOnsy3HbvNrZsayff4fxYM3mvETJ9YZ3J8VXmLxVJEkkqY7L7YB+DW/Ncnilx6tg8s1MbfOChHWz9CTKypJQEfozXCJuLG9Jorb1GSDZvs3v/j6aP9jfR3geRd2lSypY76+rIR8l2UVuY5QqIxHFENmXg+T4DhQYiWgeG3/Nria/InlzzkCVelcYTn0P0mPiqiYZHGCWsAh2KxEgECjZutMyetMl8/Wqd8aX5JkVWioStmS2Io/1EmXmKb2B/1co+YRCTzpgkiaS8cbWKYiOKaTM01NY1nT2xQHHdZffB5kPSe3/MkfGX6UkdftP3uXY0qUU6v737t5n0zvPq8ut8Z/4xMOH7Lzt86oaHuS1zgKKQtAuFdSSJpqAlAcJO0QhctCghpZhs1FYQMkKGT5DNfYpvF8v81sEe/DMr1Krr9PptVL5yjrHMFFGbYOuiweXzy8QSenUNN22x5oUkSYQa1KnoWephTIfVDE7bqqDdMllyfX7/7CwPDXRwd8+bZ04zF9eZvtD0/ddr19d2rxZXGdixnzMbNdw4oY0IuzhH7pa7No+Znp7ixRefbf3JEbtHtjE3fhy/UaPRcLm4XmS1WqVThuzRJEF59boMfACpWs0CZQg0TcMwdAQKtSSizXFQNY1iXSeV6mWjrHLwzt10DfVjZwq0p9OIVtnm8b/6a9a+9mXYfwDrH/0GSzPjrMyOszQ7wY3Lc6wi2VAUuoZ2sX3fHXQN7CBVmcf92r8l2ZjDvO3vY+x7mPWVGk9+6zztHSnufHA7bXkHO6W/JWgEccKC67Phh1iqwu78m2N6DTfk0oU1psZWuTxTIkkkmazJ3pv72TbaSXdfZvPcW7YX2LG7iyOPjvP4186yY3cXdz4wgu388FlJGMSUNlxWl6o03KvA4DWuBwevEW1uv12FVUUR9A21vQ8if6csfkMtEZo033DqPLT6QFUkZB2DRqBg6xE/AtHox7uU1o2pXaHdJhHek59HuiWizjsIgjIpPI5PNTuOLY4GNRhO5TkbnKHPMThbrOO2GEZX2CqB5XJH/gAX6gnU82gEWK1bJZUxqLRmIrqh0tZuU9q4OhNxo+S6AlOvvzxP32COvYf6ias+fZcEHVkdR3+ztL3Z8mNL0ZwMVZckDx24jweH7uWP/vQJFrOT+HaVVxdPcqi0jbIuGKrHrDuCzh3taMd8hNWBWythxgIjkhD5ONJnwV/goXad7y+E6Ps6YZtD9McB+d1bsXv6OLH8DHYDsks+s2IN+k3SR2a5Q8Lp7TZrlaZ7papnNqVOZBhzeMHH35XmF0d6+dLkEo/NrbHmBWR1jVs6c2QNjSiMee7Ji7QVHNyaj1u7SgcPPJfAq6O29fCN6WVMRSFfacqdxGHI3MTrjJ07gbksecDcw1TjFDONMc4+M7Z5Dokg1gwKhkXGSWEoFkp+gElfYd23qMosdZnBMg3aWkC9uN4E/i0IOlDYyLXRnTGpLi1T2xBs2d7Ozv2jpO3rO9XI95l75nHqO3qY7xK8+If/I0kcIRSFQu8w3rZbaegWHx1sp/eGO5CTLxEc+QP8yjJKWy/WPb+KNnIrbi3gu189g2GoPPyZPaSvSQRNpGTNC5mre8zVGszVfZZdf5NI0mHpmyDi1gMuTawxObbKwmwJKSHbZrHvlgG27+qksydNLGGsVOfF6RU+PNixyabr7Mnw6V++iRMvzXL8xVnmpovsv2UAw1RbnX/0BlBobkdvUzdGCDBtHdvWsWydXN6muy+L5WibbZbTXNuttW6of+uTG98HkXdrm6VxrwWRbuQ1o3VVSBxLJUqaICJ/QjGRK7GWK6N+/8X/Qrw4hnXPr2KocyRBFT9SeWFckkNQaE+zUavTrmSQSApGszOcq3mMtqU2R0uqVDFiC2h2NNtpSqcAaJbGpYk1AAa35snkHMZOLeHWA5yUQb2VNwHN4Gi17LHv5n6EEDReWWDLahv35N9aDyxoucKUwQzRYp2ZsVXGjl2mqyeNKNrcP/Qg508u8vED+6hOrEFeUF+ukR5MU7NV9Dgg0S1q5TUsxcDwmnGbjrjBAmCyyL/ctx9NUSiVm8Hs3JZBSt2SqcXLfEAbZuADN3PG6kAvVund28PKmWWG6jFTRtPtVzWym1InwVKNQ2shk2se6e0an93Rx3fnVnl1tYIETm3U+JWdfUy8cplq2eMTv7CP5753EfeagmXV4ioSeFnrIUokA46JfuEiAK8d+Wrz+vVB9mQeQBEKu5xbKPRsxdzbxUlf55VKSCZy+ejcd7nxH/yvSCn53rE5vvrMJO1Zk599aITBrjRtaRPTuArul1drjM0UufjMNLGlsVhscGZygwMoJEnCdy6s8JXPrfAvfv4AA5mApdlxVmYnWJkZJ+wGcGkLPEYO3E334E46B0bQDZPHxi9ytOhzfO51bj3+z8mEVUqZflZu+iX04ZvocixyITz2tTN4jZCf+cUDKI7GeKneAg2Pubq36RY1VYXBlMUH+9oZTJl0WAZGkHD6+GWmxlZZmGvOnnN5m123D2INZhjoTJPSNSpBxKNza7y+Xm3l86jc1t12HSVbVRVuvmuYrTs7ePrRcV5+9tLmPtPSNjv+VMakoyuN5TTbOrsyhHHcBIcWIDSTFv92A8KPY+8JiIyOjhaAL9LsbwLgAvBr4+Pjq6OjoxI4zVVG6mfHx8dPt973ceB3W9dxHPiV8fFx94ft+2mafKuZSLaL5BoQyRg+cdjsdGw93Kwp8V7blboTqiIIzj9DeO4p9H0fRt95J+rkl0jpOq/PdeEGCj9vv8ZG+iGgzsbz0F/Yz9jiDOpQlkXXZ7QttZl3ogUWF8dXgOZoL8tVN1P9mu8iUjq9AznGTi3x2ouz3PXgCOt+yI35ZpB96XLzAe8ZaPq/49aMZahWeMtAdOfWNoLXdDr3dLDYCFmcbsYjrtA2b7p9kPMnF3nxqUnakgTyeUTZp8PWWc56WFENz87hVi6QNzJolTo4UEh8bM1iqnKJ2/qaGeBX6L2pksML7nEUBPrEDBtqF0t9ffQ4FsauDp5ZXqenEXMho+KpJhU9u5lVv150cYCeUhMUTFXhU8PdfGJLFzPVBn91YYHPnZkhO7nBnt1dZPsyqG0m9WtmItXSCmt9+1mIVD65pZOxtQ2M5XEQgjC/lZzMsl89wJpR42g8xn65lS2VTp66EHK8w6KrcZbPTj2OL2J+79X/hHtxFxPTLod2dvIrH7kB523orf2daWQ9ZCJKeOiBEbbv6qS44fKlPzmGk455cKCB6l7mwhNPc7oVV0rlCnRpGcyldfb86/8dO319/pP063xw7vvcMfUyCpJy+zZeGPwMZ60hNoIIealFPpESbYtF7748f7Wyzvpck9kogG7bYG97mqGUxWDapsPSUYSgVvGYGl/jufFVluab15MvOBy6cwvbdhaYEjGPza3jr5VgrbR5TaoQ3NCW4lBHlpGcszngeqMVutJ8+pduolJqYJhNoHinXJnOzsybki7/rtp7NRORwL8fHx9/BmB0dPR3gf8L+O9a++8YHx+vXfuG0dHRNPCnwN3j4+MXRkdHvwD8C+B/e6d979H1/vgWXZmJXBMTyXWTXEO07U+VOX/8cQTbm+6st8EQN3T5yoVv8/d3/symAu67sSvuLH31Iv4LX0Qd2IN5+OcAkCSoQuXoTB+D6jq3WWf4jvqBzfdur2zhICobL61w6rU15P7ezczyjoXt+NeruyO1gC5dcKQCw8MZ/JTKXUM5Blo5EqePX+ame7fiRvFmUH1pvoKmK3R0p5FRgnRDyqpLLnRIqsGbtKyko7N8uJOdeYe5lAZXUxfIFxyef7I5Ql9frbMOOIsmZjkk3tig0BEjgEVFR8oEzbZQ55bBAduvsK1jmIvFaSqVEsXiBqdPvESfuQN5uoKV8blR6Sb0p8m197Lo+uxrzzBRdrmQUTlUTUBKKnoO18xSbwkP+vUAB3DWPWQiEa1ORxWCbVmHf7J7kD85Ps3qjXmCQpb/+9QM4RaL7aevyrPMF8ssbr2D0azN4c4sY8/+NSIOSRQDoWe4OdmLqwYcbZskWRU8uUPnrgWF+1eg3V5mqPQqRhKyFBU4/VwncVCjd3SVe+7uwzbfOdfj4vkVdEOlt99i/sJJTjz/Cjn9EmpQI5gC007TPbSTrqGddA+Nksrkmf6Xv421a/d1AJK4ZcLTTxCcexpCDwWwPvSbZIYPMgB8mKaUy5oX8sLxOS4uVslszRHqCt2mzs2dWQZTFv0p67okyGrZ49SpeabGV1leaHbY7Z0pbrlrC9t2ddLekaLkh3x5aoHpWoCtVMiJCR4cfohENmu3DGfsHzkZVFHEdYSH9+1Hs/eqPO4G8Mw1TUeBX/8hb3sYeHV8fPxC6/UfAX9JEyjead9P1WTcGkVey84yU0jjzT5+RSRNd9bboMhs9TKvLL3GnX23birgvhuLpSQblDGf/UtEpgP7/l9HXMnwDQOmVjIUXZtPpl5BCCiFAfkHt9C5UmN4vUhnrHFjh8OZlTpjR+ff9nPsQpFXt73EYpTik4t38JwfMpQzGDyxSgQYWYOgEvDXJ5tVBwWSF599ndPHy/QN5VAUwaUzY6SIOJa6wAOV/UTLtTeByJVaIjlDI3SuvzXXVuo0GhG79nZTnq8w2whoP1/arMyVb+WtTNZD0kCmrQtRuUCkdSFqG+QrGk41zTcvfLn5nSp1Rp0HANjqdkG4xioQzap4vQm9jsl83UMIwfatBf7xiS+RUTxMw9xUr03cprtMhAnxSh2tJ826F1D0I7ZmbSqzFbKvrFC7u4cX1ivoiiARgsUOnWOrZRpRwnOiCy0O+fS2Ho6fPoI1dwYpNNJOgQ+Yt0OUYD04TPzcApd37MRVTZYOZNh9yeXgDAgxxHNJN9+o3EjW0bj1Qyon3Sn+8NQJBjP9PLTlPvZ33nhdeeAoDFiZm2Tu/BHy5irf+dPmb4JQcFIZ2ju6GNyxhUwui6IaCKWOiC/TmFghLJcp7GvKoiTVNYKTjxGO/wCSCG3rLaAZRBdfQttyvXSKrigUL2yw+sJl7jjYx903v1nHDJrZ8tMX1jl5bG5zxtHRnebwB4bZNtpJvuAQJhGXytN8+/w8E9Vu9DgmVz9DT1Bjt7qVkeIGen8W64Y316v5USwMQxqNOvV6Hde9dnGp12uUy0Ucx0FVNTRNR9f11rr5urlo17S/ue3abVX92x0Xec9jIqOjowpNAPn2Nc3PjI6OasBjwP8yPj7uA0PAtUT4WWCwtf1O+35kKxR+/ApvV+pHvNH8yMAFcu05Utcco6TaoH79saqQ2HqEaelveb7FuAk8+TbnbT/vncypVfn01FcQSUT/z/9rjI6mVtXUv/s0NvCi/Flylsc+o9m5SyJ2jHTyoQdv4MznngFgr6qzozfHU6U65UaIZ1dQt15mIn2JfzD/IE8vGaTKeW6YuIP57a/xZP+z/NrKh+hYuTpzeiBl891qwNr4OoxkOf3Ec7DYZKp19luMra/yyuvP0Uc7L6cv8GB4E2KhTucdw9f/Xo2mC3CgkCZKXX9r/twvH2L0xh6EIpj761N83q8hxspofoxla9TrktncbrysIL0OwzfuZf6pF0hUi9LiHEu1wxiJQj5vEHoaa/WIr9U8BgfL3BV1s95Q2RA6lbA5Et3dn+f7l1YoOAYDh4eJXplCaxsiZxv4onl/zAQxJUPQFkF93eXJsMFrS01XyoN9OeYfO0snNXZFPk9rWzaz8Gt9Kb4x3XTtWFKyd/UEJ546zvz5U6AYaInkcOoe8CM6P7Gdv5qe5ULfHrKGym8e2MaezizJoYS5r5zEn78dmfjcac3ys7cK5LaP4zzeRtuBkCPlp/nCmS/Sn+7mQ+37MBdmWZyeorRRQyYSU4BtaeR6HXJ5nXRWQ1EEcSywjRKhu0GShCRxgOV0oryUoKYc+m8aoXL0P1E/8xwgyOy7h7bbP4ne3sfKtz6Hl2mnq+t6V9fUxBrPPXGB7aOdfOoXDqC8Ics/DGNOvTrPS89MsbFWp63d5r6P7GL3/l7a8jbzS/NMTp/j+JklvGKNbOBwWA7xUOhjRVBPeliPlilF5/mBfIHG2SKZEwMMHrgHVVVRFGVzuZYW7fs+tVqNarVKrVajVqvh+9ez5wBM0ySdTpPNZigU8kRRhKZpBEFAGIZ4Xp1KpbkdBAFR9PYlmd9oQgh0XccwDHRd31yuff1226ZpMjQ0dN13+q9tP4nA+u8DNeAPWq+HxsfH50ZHR7M04yb/BnjnVNb3yNbXa29LrXsneyd/Z9Tyt1bqEe41x8RGBurgO6OUyyW69GV0JcbWI+pu8JbnK5ebMYJiyWWVd+dflVKSevbPyTaW4YF/Rlnm4JrPuBR2MlO1uX/kEurGlQB8TL+uUdxosGSW2N7oQdEnsMKd3NaV4ehKhZrhk2h1VKkQ9g5waWGRW2yT4Z4R6sUsi/mn+MvOI3x29R6m13V0ITiYtdGB9LyLs1IFrwkgduclzizMsTTdhtXI4BsRiZKg5MaoX9rO7MnLJB0OVpAg6wHuSoWt1RhtqkhKU9BMlTiI6enLUuhJs7ZeI0kSvPkKH7MUHh1Oo42XOXj7EFMvnedC562IlQBf9LEqm0q7SqJSqw1jyybohUVBzgwZSducrnnMzZkkPRLdMMnb/azYGgIwvYjFiktOU1lemOOsu4XJ0n7UjXnWdYXvr4ZEq3XiWHKqy+R4rQSewkdyGerLNZ68XCI9mOMzK9/j5drtmBmPX0zXeMTvZqPi8d/u6qe7M82jn/8d4jhkgWZMINe7lR3V7RihRUk5wp+dqLFmd9JVnuEj5WdwZ1ReMmyWkgyPzHdwr9rDbaksamon6+fKjL18nOVGwMJMlfv2jLJRlbinZ5mMm2MyO2PQv7UHVXNoBGluuv1GfN/g6cfm2XbDFty6zsxkmV/6p7dv3ktJELDyxb+kevIlum7pY+EvfgdUHX33/Rj7PgzpdkoxsFqlsb4CVtt193txrc7Xv3iCtoLDBx/eweTUPL7vI2WC1wi4dGGVuYvraJGkPWuy/UYDIVxWx1/lyKkIJRZ4BITExDImkg1WomUWotOIqA6RC7IZp5IIpGaTGG2seiobJ0+SJAnJ21AkhRBYlo3jpHCcDB0dPThOilQq1WpzsO0UQlFp+BGuH5GydLYM5plZLL2t2KaUkiiKiKKQMAxb6+bra9uiKHrb/Z5Xu+bYiDAM3pKkc/vtH2DHjl0/tM/4UUxRxLsefL+nIDI6OvofgB3Ax8fHxxOA8fHxuda60opt/Petw2eBe695+xAw9yPs++naWwTWAaSZAapUPR3FSINcZlt2HVsPcd+DwPrCbImZiTUO7unG6skQvP4IucsnONJ7Lw8OXXUdJNVVUDWeru7GFj57etagVUb8k1va6XOaLqTtjR6ILyD87xKN3kRhukYSxUgzoaI2GLR6mfJUhCpQwwRfE5QGBrif+3ls4yl+v+sxtrTfxuD5NsAmqymshwmKp6BoHnpmHUUPCOqC6TUHaI7wH6rvI0m5lLQiPDtNNpBcqYA+0lqYW+BXAXcwz7HZEltHm4lg/tgajZeb9dy7GwkHY5hxdI4+c4nDa8+Rix0udu3HlTfz/NdeYZ9m4Xo5YjnYTLQ0NCpRwh25DJ7wmdQXcIt9TNQ9Os0861qVFQPaFQVDVdjwI/bkU7z4vTHONW4GIBM3k0lffGry6n+/XKU3o/PJD27HfnWFWJFcWCijBglfuunThGmHQnmRwvxRhnf/MitxRNKIKC9MEbfup0A3MJKY4WofHcYARzrXONbxYUTi03/xDPWqx9fYzXrPNA1zAyWqkCp4nFiXzBRjthgJ7fo6lrJEwVjAlw3mx8C0FLq6TSw7w3gqxw/EPBk9Yuux/Wzd2Umu50Ye//pZam47+w7v5vWjszTcgMorLxNXKsTlEv7EMQx1hc49oCQltIMfQ9/zIIr95mJZiVtELVxNJHXrAY985TSa4TGwLeLb3/oSDf8tJEzSEAErwErxyg8rIQlRIg8t8VFjjySoQ9Ia5QsFw8lhFrZh5zpo6xwg39lPKp1pdf7OpgpwnCQ0vIi651NvhDS8JiB4oaThx7h+xKIXUV+JaPgRNa9C2V3D82P8ICa4Rk7fSen03d1PLYz5nX3DtL2F7M2V2YWu69hv9nT/2BbH8XWAE8cx7e2FH/7Gn6C9ZyAyOjr6fwKHgI+23FWMjo7mAW98fLzRcmd9Bni99ZbHgT8YHR3d0Yp9/CPgyz/Cvp+utSTfxZtAJA1UURsbaO1DCH8RWwsx1ORqJviPYVJKXnlumtdenOVgxsJddtHuigmOfZ2N/oMc7bydD1/jTw1OPsZKnOV0OMSD1mlS+lW3gdoiyCUt6qsgBCTp4RpHyxrRWoJUEop6nYPdN3HmhIuiK8ReQklIUprKw/s/yMQXK8x3HOOS/QKzB1Uu+Fn6shby0i6UWMfqmMMv9iKUmHW3HQ34x4Usn1uv8ES9kz2WTR6XJbXMWKGdsqGAJthQJA+XJPlys2N16hEfLKThYonKYp2k1HQzeAKqhqDDi5Hb27lwZhmztEzG1kirHpXoPipOL2d7PoAXdaMJQbs+R2+mgy1ROzoqR+NxpBUAfayHATtT3SRezNaViMVyzOuVixjhBtHZec7PJwxZ89y5/Wb8MOFzgzqDtZiVIMSqR+xSdLTpCsari0S2zqMz66it/zw37rN8W45gNcuX5u8iV1+im8u8WnsF6Td9/hUzy1RvG7cn/fTWd3A2Os+xzkOkqwt0nC4RN/JkVRCqoO1cP1XNJyUSzHgNU53DNE9RimqUYlAVlVwqRX+PQa5DIRV3wcIoZ6ZTNBoBd2T2gLlMJRTMVh/hu4+dIjpW4bZUhYV/+3XqSTey8zZmv/BnZDIB6X7IFgDNxrjpo/Td/QnWq616J8XGZiKdVw9oVDz6qgobUYHifz6J51epyHVMp4gnAi7NCrrINus1LgAAIABJREFUMUovhtQJEkmoQd1xWVBWKCVForBKSkAmFIhajaglsSIUhWyhj3RhFytmgTNKllu7hsig4XoRNS9kpRbhrgW43jKuH+F6EXUvpO5FeEHEDxvLmYaKpiugKUQKoCkoWQ1NM7B0FTSB0BQybRZ7unKM2OZbAshP0lRVRVVVzLcXRvivbu8VxfdG4F8BE8CLo6OjAJeAfw/8cYvmqwMv0nRnMT4+Xh0dHf2HwCOjo6MqcAL4zR+276dtmxRf7XqtHmk2aa2mv4GT3kEiCzjRHFEsNuVJ3nSut2m/YkkiefbxCcZOLbFzSxvbPUlglmk8/f+idAxxad/PwVIN9RoqYrw2zQ/kLShCcrc1hpJclc5QRRM8pN9cN8QcKSEQq1O8tvUg5iWBFAkJCaP5EV6olZq5BV7MsozZmbOJZMRvf/ajTH35KHNVj40D9zI1c47XM8vIvSsosUI61rE0SU89z0LDZp+lc9mvcFdhjZc38vzeRo2bOuewQx+73M6pnTeRLs5y33JEPumhkpT5XmfM3nmHnY4GoUpyTaVGXUI2kHSgkFuo0eeeRUkCUBI0pbh5XNHqp1k9Q5JwimrQw+H0/ZyteWwYGkKtoGllVgIDReQw5Rx24zn21Gqw7vLrrf+nkXewc1nU9u2cXUmhxBpzjsLNvkp/T4rYnuebqe8z7hXQp0bQkjSiMMFSIWDnto+yHIabBAC/eInu+HlC0iSyhzDpJAp2cn9scHPOYdItcSpS2PnaaRrFPNJQMfIT6OmYwPOw/RptYQ0RNxAaSKEQiRQy1UN7QWPbgI9tSsp1ncmFNEa1g2GR4tY2h0p6luK6SVztQ0887vrBOMRjBIaBm9j4O3eQ6AW2uaco3GqTjgMawuK0PsQl0YF+NqJw8RiiXKY3UjGVZgVEXdEwhGRWLDKv34QeqWxwkrrqIRB0GHm2Oz24JYOVtZBFQpazc1TUeVKNgLZKTDp0yUu/9VwIGnobrj5ALddOWWtn3U3hLcWwBJ139BJWA770neuLwRmagm2oGHqzvosANCHR2k0c00HTBRlTJVYEoSqQuorQFBRNQWhXKxp22wYjWYftWYeUprLS8FlqBJiqwmguRX/KpLsr+z7Ft2XvFTvrLPB29IJ97/C+bwHferf7fqr2djORVgZ2KiqRcXSWKhkc4Oh4J0M73vpUYQuQDOXNo5k4Svj+d84zNb7GLbcPsbPo4zaK6JWvI3QD+0P/jLDU/NGvzDVkElNZW+Vo+U72diyRjT288Grnq9BiFdWaDIBJO+KANkC8fJHu0VspRpJYjRnKDHBD+05qlefIZQyoRPgqnFs9wrPTJ7lv8G5Gb/ksB576D8QzC2zb6Ob1sEDFalCyXEpWnbB7jtHJnYTE3Ldf0DNt8Xjkcihf59XiAGeLPRwsXEZZXuVXwxXyoumyWg3meL36FF2hCVuGqHSvQaxirm/HWr4BKWWT658080zmJh7nlJmnt2Mbss0H6uzQprkQDQMSRylykzNHSc8woh+mHpc5W2sgyJLRjiGkwE32UwpcTNXmciqN6WWxwjTtap65uIYixhmoLhOf/WNGVIOPN/qo0E2kp5hSTV5svEaiSiZTl1F3rbJ9ag+Xtl7gcP+tLIQwknU4cLjKo7MNtp99Ed/KUdLuQoQ6sWGRTeskGZMnCZhNa0TKEBUtwOxaIVVdwi7Poyy7mDSxyDCyOCKHUq6QMi0yt24nZVxGVz08V2HmTEB9ag3bByOaZlmOYw/fyX3pncxp55hpTNPI6Bw7dCuJIZpijDJhKF5lT3iatOZRjS1OqVtZI4eZhAywQl9wjm7XJNF/BjQHkmUSWWeKMuN4yDjCSiIaCUSJShypRGFMPZljnikUIlQZoxLTV1LoRUVikCg2NWsHgZUjNLJEmoOQCiKRiFiSTSQ5QyLaZLPt5AZWLJG20WQ+xrKpRhqBiBKupqS1niWzii1WSAV1jDjGMmIsPcbSIG0YtLWlmrG29nvpLwyReUOc44qY6Pv21vZ+xvq7tLeaiTx/ahG3EoCUdClV9JTBRGCjJhomEUEUvuW5ghZd2FCvn9WEYcwTXz/L3KUid9y7jRE3Jiw3CMJHsUUZ6yP/CiVdIC6uoYqrI6hk9RLP1bcSJoJDfQswB4p3lWmitkAkmp8ETMbsmH35YZg8Rtc+nVIMiRpxV/+t1E8/zW/pj/Cq8QE+kn+U8fIN/D/WOjkjw1NzP+AIz/OhfA/3zBzjlHUHCgoDNYtD1XW2JB6m9jD/sSZpN6oMXPgmUtnPR5UcsTJEb9bmG2WPUysjPJxxyNGcm58OA+Jem63bh+nKlwmjIurqMKoCi67Ot1ZLdCUeN2o6Q47Bf3ZjagN7aDddFoRKe/0cqpSUaX7nm51X2GZMoKlZpP4gjsjwSu1xLE2lHt5CQ9+LYgWwAU8541zs1xGpn6dS/SJaWOKBSoG73DuZVrfyF8rTbPFchqOAbdUZLDFNIuH5wCbpzTCQ2BhBijm9zNToOKPKFlJuH7LhE7PCq0lESi0wffOv4OomyTWzx1VgUkpMt0amtEC+NEe6vIDauj9Seop2e5iCMkTBHEYTOlFQZ035Bto+BcO+hFtUmRrrYnW1HXQVxZGoOQ9F90CPmNMm8KWKLAAyjZqoZBIHwzNIhSt0M4sQsJZ0cybqpCxtEiGpYxNKlVgqjMkbMKRKLJcISRCypRBMDsgjpUodFdlamjoHze/5Q7lKARiVK8pBLlKAVARSFc21cuW1gtQEviHJRlUKwRqGErCW78BUQwwRYKo+luFj6D6m4ZNO1VFUUFXx1jXUGxsoCLakBPa7rEr5vr0PIu/aXjmTkA+2sLs1EylWff7y8TFu6V0jr8R0qFWkY1D3YwxhY1JlY+n6zD0ZRaCq+MmVTOerIBLHCd/75jnmLhW55+GdbFUUvLEltOzz5P15zun3c2tPc2oTS3mdK8udPcdz3i62FgT5XICcFyjeVZXecmmFrJTES3PACFNWhJvvxQk9+rxVLiYKiRKx1xlAHv2f6NfA8Z9HFxF7qqexuwb4Hzp/mdKJGUxPQZEKkSH5mDRRUK5qowDTQchSVOFncz3E5j9BoIEMEYTcYknaVYPHqiFfKtc5UlvhYHaaniHBtp4NGqHCEddnfMNhcKaHvVPPcvbQrRRs2Dl1ku7yeb7ddTv2sMOg5WImCbLSpDHX5C68aBRdmeTP/Rv4dPoA0qmSaDVOWD9guj1gl65SPxmTqAVUaw3LdjFrOXbOX0Qv/Rl60AAkdV7hjFlkd+ouPhV/lJfcH/CCbpJkU+R0G8uwOddmk9NSpCKLqpPF0RwQgnlgPgFMqCRtpCNJWkravZhULSAVgd2oQG0erzFHNVhEtu4HS6Qp6FvpsPtp13vRhU7kF4mSImX3DKVuH78vwTX2UolskvUAY2MNvz2BtiJRCMPxMoPxKpqUlOI21txuqnGKctxOLWmyb+okKCQoIsUUW1BIEOJKW3OdarWpxAh8VCVEIUaI5oxCEXHzWBGjihiNGE1EGIQYIkITETrNfVJAgkKISiA1PKlSFTaVtMWqqiOzy0hLoSexKUgVXU1Q1QRVSdBE85rUJCZTqpHaqKIqMXGbTqM/T9h++U3PahgmREFCGEjiWCClAkJHUS1UPU3/yCHsbB+qnkZVHcS7LMR1rcVxRBAEBEGAZdnXyfP//93eB5F3YVEYc3pSBe5jd0s768iJeeJE4vkhmq3QqVRoODp1L6SjxZRr1KsUixvk8005+Muf+49ElTLuLzRVWq+AiJSSZ747zuzkBh/88A5GOtLUn5pCtV9Crh4HYMm9qhAbJU3XTuPCBI0LExxbWaUut9JtraEbOjLXhlK5WrhpevIcJyuSkY0NepTt1NSAUq6AA+Q25gATSw1QHvldJPCqv5WbzataQv/z5Dxy6gs42kcos0bFsolUne5GQo4UEggdDcONeMqrYgrY62ic12cp1Sax/UmKmXZ69t9J6sw32Wvt5oP1Gt8L9vN48UYOmIs0Gmt8t61GBGSjmNvOf5uUXyN99Ale2F/g5L4y3zVHMBc7uMFaYzSao+Atcjxy8OJtePEe6rkVZkYuoKhjfOPaP1CCEhlsYGAZEZYqiQBFq1EudpBrhES5EcikCIwUnuWwnG3nRdOirqUI1V/YPNVia51IDzNokAQlBuqrFKIKs4bHhB1y36qCH/aw4JpopVXidAe2bGCGy3jxMtWk6VPXhUW3PkBB7weth3kjxTlDsKFKKkhcJLTlKWRSZIyQruoS2lJAEOoEoYrjSixXx5YqA6JMj75Mh7ZOMcpRSzJ0a2sMO1eTSaV8c0nl99KkhARBhEIkVWIUpBCoImkuyCZQIVGkhCvjnDrEpkZl3xBvnLuIRoixVMZar6FIScMy2WgvULczCJnCqGUxnXYMK49ut2E6eSwni2mnUDXjHZP5pJSt/I4GYRgQBH5r3dy+Ag7XtkkZ47qNzX1JcpW9lc+38/GPf+Yn8Mv+zbT3QeRd2NpKM5agEoGiE0Yxz5xYQFMFSRKToJJXXXQL6o0IuyDZ01PkzEye73znq4yMjHJw207c82cBWD3yPRjVMFQDKSUvfH+SibMrHP7AMKPbC9QevYDirMHGy5vXcJ/+VcLKbejZDmIpaSutM/f5zyM1wdPbPsmg42EmRUxVEDkmRulqoHn7tm28supyTIUMp8l6FusK9Ns5lJVJHs5OUqitI1WNR6z7eaXaziHj0nUdjpDTqLdG9F2+RIeV4uiqyzgqGQLuSXZilY4g5BL/0ITT5hBfGTjEmt1FFJSpxxtIESI3niVw7yUpdXMwP8ev3XmS0pzL8MoSz8/ZRG0pzCDhgVfnsYOQsV272TV2jpHZiLEtae6uCLKZs6x5OdprG5ytpxCyD1ffjy5WGF1+nlpqhLXY4gYlz91GnkfWYi7Wm+6VCVlnfyrB902C9gyKjKCqMDPwMNUtaZQ4wGhUMH2PeizxygFGkHBY0WiLJMVaQC6GM5lxJtomcS/eiaoGWPoii2pCys4jUjCbTuipLNOt1UnsCkq9SUysKzr1bC/1zC5iJ4dJQt0rsxqtosvL6JHPvnqZQlRHlRJH+lgiQF15ByJGi0aaSMF63MGx+mEmww50bQUdHVsJ6adCN3UsJaGBRUnN0BAWkSKIkOxKtmBjccacZ9moUZc6mbADRVcJLIeqmaNhpokVnf0zz2P7ZUJdR+oCYStoJpgmGAboOuiaQNWuKVMAm3TuJJFEoST2I6QXofgRStCUCAqmSihBjBZGWGFAKo7QW9QqKQShZhMlFtP1QVYq5ubZNa1EKhXhOA0sq4RpmmhaUxSxCRL+dWBwBTCC4KqW2duZqqoYhtlK9DNIpx0sy0HXjevaDcOko+MnV5vkb6K9DyLvwnyv+QiYio9QFI6eWaDWCPnEncPMjs0RyubPaQUl/DDGNhIKGZ90LketXObixXEuXhyn/YadjB6+g3D8EZRE4p05y3i1jdPHL7Pvln4O3jJA/fGLEFcRwbdAbwb2yt13k5v/Ho0nP4/6od8gSiR9M02GyuLIMKtRlk9o4+wPxxBRG5H0uDbakrt8nIf6bmBqpsCE5rJjo5dLR8/T4QzRtXiMjCY5a/ViFrZx9GwaTbq4SQpHrSPFThTZ/Cz9+T8hpum9urN17pKaQo2PX/d77WUWbT3FS913sJy6k0J8C0O1GRoTa7xW6WZEX+T14gALR/OMJpPk9Cojlol9ImTgYhktTJgoDDBXU5Gjo6T9Oh+7sEi81qzLMUgz0DxotHG8/yBOWOHgwlMIBex6g0rvdu5pU5mu+TjmOjfbEYHUOFfsZaWhYTo6saPRVTlHQ8vQPumydfb7LeozJELh2PaPosXtRKbGUUMgDQ3RZyJUgRCHyXIYcVNEHHqsBG2ky5fpLM9x/+kV2qIqjhKj0KyyqNuSnIjQSdDCdbTiGSjylhZLhY24QCxVVpJOYgxAQwgNFAdV6KiiTiWZIUbBjbKUgu1UohRGUqMjvMQN4TRp3adQ8EnlINFVlvUeLuh5EtXAUsBSwNFiDD3E1ZcJ9YgtAragc7SUY93ahSlcckqVLmWVlDKLg0dqT4girupMJYkkDJtLFEqChoQoRgljtDDAlwklBOuKyoowWFdNBDp6YqNKSZ+f0ONDjCCSCmkR4EifCpJA0/HSPSTZHmKhXpOcF6AGAXFLmj+KIsrlEuVy6U2/pxACVVU3ZUaklCRJgpRgGCaqqrZkSK5ImRgYhoFpWhhGExyuSpVodHTkqNXC66ROdF1DUf52S5j8OPY+iPwYlqAgpeT7r87T35niwVsG+auJGC9p+lTjlsy4Y0iEgGok+NjHPsOjjzSlvTc6O3jp0gTC2MmNKx4vPvUEk7lb2XljN7ffu43GC/PE6zU0+zFkEKD27CQpL8Puhzk+scFN4jXcb/4fbOs8QOrcMfSOPM+ZhygkVe5RX0EIQVkrsO624VBC0gxvyvoGwYXjDKq30hkbfKltGcfL80qU4pNI1qMCU9ENDF2OuCfVTY+jYiY3kyh9NBSFMa3E6kYDJYK+VMxu3M0wSFvcnKUt2Qf53eIB2jSPf6P9F7ZVFhlMPcJSPcsxsZvJjj2sBilyqYCOsEZJq7FSS7Ms9/FycgP3rx1jX3WSanuGhb5+AstgxAho86rsMBbQu+C1fCdKGRYGd7BW6GMjPUBkaUgjZtz85yAl/825v6A/+DJEUFCy0PsRFguDOLUNduVWMJdKZP0ltAtrKDImIcaVh1lvu4k4ExMrBolhMiyqpPzLpOpVtNAnFbnYSQNdNsgmHhYxGgmqAEskWKL5n6Ox+XTFUhAjCFEpJzmCxCHBRkoHsEFYSFIowkbXVEwlRqgdGKqNJaGTZqEwqQZILdhcNxSPaT2PpkWkwgZt+ipKZgVTDbBFjK6mgTSSpoQEQAYYxSWOPfxQI4xUYikIIg0/0HFsD9tqguhtbWXg+evufSkhiQW1skl1fgAp0yQo5EnIITCExkwyRkXE+EInFDqxUGhCqdi8hqsiPxJkk+y+qLQyzoVg7Y0f6gnwVngrE0KgKE1pE01TWzInzc5cymaCXpJEBEGI18o70TQdwzCx7WblxCagJIRhiO97RFFMHMfE8bsr5SCE2ASi6zWyrupsjY7upqOj60c+5990ex9EfgxLpMrEXIm5lRq//PAuFBmS0kPWg2YGb624Dpg4RvPmCwKPF86V+cx9D/PaH/0+tb17KcYBYRTjRBaLoxZta1Pc6C3jn89TnJ3GyjyPuj6L+eA/xT/3FImVwjUqnPf2Mnj4MOLin/N67TXuLLo8sbOdS3WHv2e9jCok6wsaykHJonDoFwJFShKpEmqHUdRDKELHBH6peiNSgNuaQZxp7EPRTdrMfrYbOppUEdpNkCxhyjb2hj/LeeVlluRl5mp1ZvReNLOHB4QPiuCRgX2cVPqJLq/QlzNYi7pIiXVmjvlQWedwNMWNyRH+bOBn6KgtcDS9gyF3kY+tPUNnUEYlIQYu7x3ikD3LTlqFlyJAgzCBx1JDtK8bvPDhj+HbKdQgQvViuv1FOrwNMlGVXKNEbmGZcg2quV6euvE2Iny2TzxBoTKHk/gYQqIaBpom0UWCIafQ5DqW52IFVXQh0d9uQCmabpVIOHikiWVEREQNnY0kQ0KKWNhEwiZQsqDpJMIiEQmJiElExFatQEpVrgOFQK0R6z6x5iONS3hqgK9GKCJCVeK3vJQ9QBQreIFOHEuiBtQDhWJg4oY65dCmGDmUA4uqb1D1DBqBRphcDSL3Zqp8cuQiUaRSrWeIfIsw1LE1iRLpyERFSgWZKMRSISYmIiZRIxJlg1hK5mOabUKC8s6yGQKJToJOhJ6E6DJqbssYnQgnnSU1uBuzbxe60ZxLx3HU6tija7bf+DrabLv+WIU4FgihEEVqq605m3Hd2jte6xuts7Obw4fvIJXSWVurvEm65KrUydXPiKII3/ep1+tEUUhvb//7IPJ33RIUnnx1npSlcdvubkqt2hSxnsP1bWqlItCD0/IlDXfbfPXoDDsXF6hqJdYLdWZqRbbWVKKkDaHA+M7LHNfrBEuvEfc2ee6Ftl5+48gf8kzO5rm8A2OfRxxSGPMUkv4M903WMC1YkjtwhM9t9iTnVINOReCcucwtjQAhIDI1CO9hMdJZrH2TfZaLraSQ6h5Qhv+/9u48ypLrPuz79y61vaX37ulZAQwwUwAGAxAAN4GLdLiIkmk6pCVLYkLRR7ET01LsOImPoyTn6PgkR7GSOPJxElpUnJimREZSqH2JRa2kKIoGQQIgARIoLLNhtp5eprvfVuu9+aNe93QPZmn0AJztfua80+/VrZpXdV+996tbd6NtTmHwOFnsY5SXeVKsMELE93IYUT3NcvUEy1lAO3wfh1vv4q7GWb6ZPUsnPY3pvsyXZZsj8fuoooCxb52nLwQz2V+iB/NEk5bovCUdG2U87DA5VvCPzP/HL8j380Bwkvd3vk6Qr1LubyNGPUb9kt32JBaPPx25h282O4R5n8KztLqWd3ztNH/+1/8OaIifeon+Usj72l+gHc6Tew0q38MgWZocZzZapOWd4WMnf4v18f42NZqpr0oLBIVQZGZAZicY6N1Y2QQaCBoIIpQI8fDRwsfDxxc+knowl4yCPxffoBVUTLRTPK/CUwO06hOpBbQ0aGVRsn6sDbR80XidQH2tfvGX8qX5EeY6LXq5Ryfz6eY+/dynX2gGhUdpNg9mKDEEskJp8LTFVxW+qmjIksnWKqHKaYiMdrVKpEum/B5+17DaGsFTEDb7SGlRwqIkG/Z77RjM+jFAXVA4+0JIMSjRNiWwRd1Ky9altPUggcUPI3RjBNkYRUQjyMYsIhpBRMPX7SnkyOV/YK2x2LzCZiU2q7BZhWz7qLHX1pejvp1VbQo+ZVluCExrrzenjY9PMDk5zfR0mzB0nQ3BBZFtMVby1Ivz/ODb7sD3FN1ufTIVBHTUGN2VVWCWyK9bmDxwZ5vf+Ibhl/ynWHn3GHAERuB0G+5ZUkwO2uxfnuFM6zx2YNhbLiLFKGO6QbM8ww8s9iiLPWS7dvDK0oDZhsc7F5/nDgacu7/NKyt7eH/4DIEoua+EJ1sRcwsegfBpK4iasywtJ/SqFSLhkarDlDzDSPkVLF8H5lnkPiySB8uAcTnAqv1YKqxZ5kR5N6fzo0ybP6HfeB879Q4eC99LERQs5ad4tvdldj73m7TKgD+27+ZtjVPcnXZZHR1njCWCt44wW3Ro5DmZ1yBpv4UPpoLZXoSNv5/iXo00ksIUiPIvKGyPJ7y7OCeWeXBBMp1GjBaGEMPgkXF+8NwXGOl28KuMxkS/Hs6lgmbVgbSuJ8nwyMI2diWjUE3ycArjjWJUSK5HUCJCiwiPgACPAI+Ry3wdKioKkVOpHKsH5N55cp1hvRThZZhwle9trG65xVNpBHmpyErFoND0C49e5tHNfHqFx6DQpKKgDLoUpWS5E+BhEM0lqrEOUhgOnJxA0uSMuZu93kneEn2DhshpyIyA8vL7cnFfvArI6j4Yy7v3YpSHqSSDImBEdJBViSgrhDHDzn+m7uhpLGvzPgsDB1ONrCZAjMJERHRPjGiODANEGxmOQNBAbBiS3hpbB4O0DgomK6nOVtjjc9i0wqwHivLCevmrS2Si4TH6t+7fWuavbSMESmmU0lx0ZeG8Ri6IbINBIjC855HdnD17mmef/SYA53uCfnuCbreegDHy6iASeIZ33t/hq5HgwHKDsf44d64onmAnH27/KWr6Yb44b9nZrZvvVkxSAVPFCQxQIXlH5zRfPjrOA7bkA0tfA6CPz5+lh5AY3hXUt37OVZq5ymN6XLFwfoYVu8rC3DnG1DhvjwRjKkWYv6Du1uUjWAEEoenyPc0vM6kaRNZC+RWgpDA99tg57ooEI3IV7B9BYcFWKEp2iZJdrQqBATq8m9+sM2nD93J37xwGn67YS6eI2Lv0baTN0TbD7xcEIsejQIrhvWcB7yhf4B1rczcpKKUiVSHaGswgwpRjpEKRG4k1DUQVAU2EaiJ1E+1F+DJENy4TGESB0TlWZVjdw3gZA52CVwcHqzOMzrBeBhtvJRmBqHxE5SEqj3mb0csVnc4kq2nA+UHAahowKDzySmEMCCMQVgAWIyqkLvC9AZPhGXzbAV3QbxhMU9D3YZGSTe2FdsBal9FJIZgSigfClG998wHuanZ484ETSDtJUVhWSltPAFYaTFlfQRsrKKyiEJoSyYTuU+iIlXCUpWiSZW+M+XCGvm3y4LlvsvPUWZ5aegsfHPltRvR5DIJceqQyItMjVEIzm01g1WMYpchURTe0NEKfMJfY1Yr+NwVyIqrnjLEFtlrAFhXkBpvWAYPi0iPr1p+5QIQaGShEoJHNBiJQm5aJQCN8hWx9d8evcjZzQeQiZZFz7tQxegMIGy20d+HXcOPkUo/G01TZKn/0R78PQDv0WT1XUsxM0VsaVqx7BRiwJmdOPYMyMNqbpTkY5Ui1l1P3/DGNTotodSePGo9vyOP4tqDVLFnqh0Qmw0qfrvGYsqt8KPva+tAly1WLJRPxRLaftwZHsFryJAc5nffwvD4HdryFh+R9SCuoKNHF76LsCoYWxvos2xmMzZlWHVZoUdoOO/QSnk0RRgMele2TmXpQv0pF2KqgLLtYvRcrNR5B3YmQAmtToAO2hyAH8vVxcCxQUeLZM0xaKKxPbgJSG9IzI+RWUyARokFDNRFiEk8GSAK00EgVInWILwQTVmyqtN7ImAxjBxg7wFbnKMoBeTWgKvvYqo885GNHSqzOwMphIPARpbceGKg8TG+srmguNf1c0809eqKk15qnpweUWYDOIqRRlH6Phl/Q8EvGogEj7WV6MmOVnB4lfWvoYhlgSbHYDSWEi6uJPaMYzSN2FhGmVKjVMWa6OxCqQMqS8X6EyjMK0+EZZrBUBNXLfOtIROlpcs/DaI9KeVRh5hZZAAAgAElEQVRKYXxNJTVGaYysnxdeyMrYLAxLBFHWp5ENaEvwZMUT02+FaQgXUn7r5A/hL2V0ZlvcPfoSBwdHmQ934VnNeLmItI8jyoCKEQrRYj7fgcRj0JKkSjCocgarBamCgRKkWjAIIB1XIBRNAy0ETSVpa0U79LhnrMHUeJPKkxR5RZ5V5FlJnpVkaVm/Xk2HyyqyYdraa+1JPvi3DuMH7qftu8Xl9EW++Re/w0vf/DIAXhDxob/7T/GCuhH+3Ev1nAxapDyYPc+f/sGXYdjLdf/5I3z/yeMcl+N0VAOBrUdp9WD+9HGOeB2mBg3aRUmU3sHdBy3T/XvRq22Mt5s9+W+wl1MMZt9EPjKHeHqBlp+jNEwMr0s1F66Ix1SXr2Z3U6KZbHb53da9qCJlrNNhV38U/6VReo0ukgG94klm5SsUwPE8x4gRnsv38YOtr3E2mObT9/0nhKs5+54+zZvHKvYoxcniReY5SagskTH4tiJXYygMViyirCGiILAZ+PUP1kAFnAtmGKiApW5IJwsokfUP5/DH01gJaUTLeoyJJuNekzEdMqICvOGNdmMtA2PJ1x6lpcwyplYS8ryPLVNMmWLLDFP0MXmfquwjGPaiW3sgQEis0hgvYil8J8veJF2jGQhJXwlSKShURakMlS6R2hB5hlDnNNWARpgTGcNMbx5RVpyV5zky1idXlrCURKWkkJZMGQq1oRWPhajwiYomXuERFgpRhFBEiCJClwGq9FCVh6wUqvQRFw0/JzDkOqWigTWKjpHU895PIql488iX2eHN1XUOtkLnFeSQS59UhQx0g4EOeao9yreiNrJUyErSfnFAe7FNPqLJxwMaz2X0dzcQ+xvsLQwDLEujPnPTO1CDEp1WfEu9iW9NPIxQFqskX9gjMXJzXcwlWYtf1Q9RGPTA0JQSYyx9Bee1pEBSygoGBQz6+M+fZvSlVcLly/ffEAI8XxMECj/Q+IGm2fIZGYteNeGV88YSr6X52k3kTuDodialyrMB+eop5s8torTH3oNvqlt1nEn4rc89w0I5TSS7tGdeIhead2Qv0jWjBAvwxZVRjs/sZUQPeLGY5aff+zhKWj5/ruSITnno7B28Pb2ffbruuW7MHKp8gtzOUe70KXc0oTJ4nQG6kyIqg5WS3ChOqBls4JGVPidPN+kPKs5VlkmdMtizzNGJPm/7dkaUGe57uYdQgu794+jIY6c5z5nmLv509/u449t/wJRusSubw7cFuiEJTF5XhFK9ahRNYwWl1OS+zyAMyUMfE3qIQIEnKYRHWniMNVJCfSHIzZ2POHJsGt/4BPj4+GihGaXBTjFKOPyiV9aykmWsZn36aZde3mFQdkBUSG0QHvieYRcpvgrQuoUnRwjEGFK1kTpYv8/+71TBEV1S6RKhCgJRIo2lW2qqEsoCiuEjz6FVSu4qBd+xJX3goOnwH9mX8FQJyqfyFJXKKUWTUihKLKU1LBvFC82SMyNd8rrlAsL4qFKji5Cw3yYYtFFGEYiUUKaEssCXFaFICegixdrIvnXppC8DetJnoDwyJRHSIFWOp6HwDJ2oYKAHYHrI0iKFQnggRIBCI/GRwoNKo/oavarQqxpRBiyrKaxSyEH9+UaTIXeMhuzwNG0lefFcj2Shx4EdLb7n0Cwq0mRK8FSa8nxZ0C0q8n5BlVXIqq4XEZVFGMBaGkriVZairDsPiqxCFqZ+lPaSI7P6G378fU/haUkVSJZaisWG4sBqxe75DJ1XjL5tD+FkhB9cCBqef337Y1xp4rqb2YZJqe4Cjm1lGxdELnKym/LCIOPhkQZjvsYsHCd/9gv0k2/w+eWPYpEIUdLc8SLve+x7mD3wIOc7Gb/+xZd45pmTNBVkaMbDAX/t0BGe7lc8K3uMzu3DW57BWM0uwJPwtuCLPF/dz10PnmN8rENv0afUHqOjl2i3Y+vRSmVV8crpiJUVjW/qsYwCndPyCkS/pJT1rYMFO86inWFQhWSVRts+O02P6ajLWJRTej4Do1g1TbrDQfb8IkVpS6ArQm1QyoKSlEqynIac70cs9UMW+xEraUBWXijI3tHs8+HZAQ3ToGEaRMUo0tadugpjKGxJZcph49CSjAwrS6RUBGKESESEaDZeQ5Zrt4GEJZeGzBp6WDJTUZUFVVliypJ+JVkqFc8V0LWWxyLBD41IbHEcW3SZK1J6QhKoAisUnijQImdgPL7SeYwJtUAgUnIbkNmAfPi4PEtbdmnIPqEcEIo+oezjqRxPFShV0BaTtOUhlFhCll9CUNeTzbX38lf3/ATjpsCTHfLz83QWz1IVdd8M1R4jWw0olnyqLCIbD0gnAlACpRVWCXIBRgk8JZiSip39CjM/YHU5pZsN56kPNBMjAStFxdJyitaS++JpHnp0F82JBsJXiOG4a2Vp+NwvPE6/l/OBj9zP/nj6kkd99tQKX//ycV45dp6x8YiHH9tHdyVl5fyg7rQX6PXgEISatJ/z7afO0OvmHLxvmkPjDexL5/GloHFohuDBGeQVbjuZfkHn915AhJrWX7sHoeUN05HPBZELXBC5yJ88dZyvpX0+fPxx9vaegGEnuleq/Xxp5Xux4SoiHSFqWXp+xLHVLvgDdo92kRaqQlMUPkUV0C0lnULR31B/GJLzN6PHKdQkg/YIhw6eIvBLnjs6w9Gz43SKCBMYpvwlHlUvMWG7WDIyWdJTkq6WrEpJV0XIcBxftZBVSJpKBqliuR9wphinl3v0swujqF4sHPOQDa/u3FUPdsRuewYqQyfzWU190nJzhaXE0tAlLVUxImEMwYgQNETBrNdHyZSeLMk9wUBVDHROT+WksqLCUAQjyKDBzNyAfXOg2qMcnQmpGGCqAeQpfn9Aq9+l1esRDUqaecZY1UNqkBqEGv4d9l1bW76WJuSVx4WyFiwe1mqsVXwnvZejxX48YfFFRaA0vijxlAGtsVpTepJCa5Qo2DM4ykS1hKBgtfSIqgW0TNGyHhdKygoQWBVBcC+Yt4OylP4plrs9lqxlJfTolBklFV4jYMeuXezavYfZ2d2EYUh3NeVX/6+vEzU9OivZprq4K2nNNLn33ilGRiJeeHaOk8fOE4Saw4/u5vCbdxNGl6+A7qykfPYXHufRx/bx1nffddn1pqZaPPFXxzh1YpnH3nM3Ur46s6vK8ORXT/CNrxyn0Qp4zwdj9txZNxr57f/769wXeOyoQPiK4PAMwX1TiMvcgspPrdL7k6Ocqipe9AQf+fGHt5QXbzQXRC64oYNIHMcHgc8Ak8Ai8PHhTIdXcyfbDCLnPvc/EfWewyIZ2Bav2El67VFeWIkpVkPmmkuE/TFGrCLFktPH6JRK5/QKQWXqGQQlFU2V0pQpkczrEUk9jfA1vqwQsqornI2lE06z7DUpswE6zWghaAC+tShr0LaqO2WZjNBkzIplKitoyJyWSGmIjI3f5V8wH+RYsBcVaXRDowNJqAy+LUkXcrJOST4wZLlBiro/gB52/tLCEImc0OYMgklEoFFBSRilRK2CSJZoBaEsCFVOU6eMeX3aIq2PS0BmLX1TP7LSEKwMeOCVBbqqHipcZBXytKE4axEKGjOgNlz426qeMtuaSz9vzIDXhsoIKjPscW0VJZJCaDLpk0tNVzc5NbYPazXt7hKjnRWibgcvLfB7Pfy0QF4897byEUEDqgqqHFutTcU6/CM9xh74CF57dn2TCstAWkphufiftDBqPBRXvk9vqHvNowX4iqwsWV5NKYyhtJBbSWE0hYXSWiopQUIhBCmWrifoeAI1qPBWcoKGx8Nv3cOhh3dtuZL5V//1E4xORPzgDz1w2XWu9ONpreXl5+d5/EtHWV1OOXhohne+/wBBeOH9/+z3n+fEkSU+9rE3kT55lvJUB9H0iB7ZiXfX2HpJw1rLyWPnefxLR5ntljzQDknjCWbfvndLx/JGc0Hkghu9Yv1TwCeTJPlsHMcfA34ReM8b+YZfSg1B8yFO+Tsp5qdgVbLSTfHyDlacpN3v4csX8EWfcdFBieGPkKEeTEoJhG2QRXeQySah8vFkSURKJcA3Oe2qR2UFgckJbUY0OE1ERkPkRPLSc48goFSSvgpIRYAWFQtyggURUEifSmhAYozgrnKFewfH0YvLBN0eq1qQNgSCYnO5ZNhp2WqN50l8DcpXSM9DBpqpUUslLQNlybDkop4PvaosMjeQVqS5ZS6tONcp8Tol/kqJHtSDXBglqQLBki95YcRjT1gyKgwoQblHoscF2fGK3pnLzPwoACkwUoFW+COjeO1Rwvd9P3bQp/etb3Fs6QznJidora5yds/uuj/D2oWREATL52l2e3hlQSEkRXOU+C0P0/IjbNDAtkYpFhcolhYolhexnRWq7ippWTEQhkzWn69EIpBY5fFcYx4pVwiMIhr2MQmMh0QgEOt/BQKDYJEMD1C2DipptcrZwYsoNEoE+CqioUcRSGSukAOFEpoxz0MLf7ie3tTH4lIKa/idasDivaOszjZYbhqq1R5vnh692mkPwORMk7nT2/thPHV8ma/++RHmz3aYnGnywR85zL79E69ab3q2TfLsHKlWtN63n+J0h/Qbp+l/+QTqO/NEb97FQmV4/EtHOH1ihfZIwNS770Cd7hG+dB5zeBbZdE16byQ3bEkkjuMZ6ul2J5MkqYbT5C4CB5Ikmb/K5neyzZLIZ372XyJ9jxwLpGhTUrXGEV5AoAxaVEjquRS0LfBNgWcLQpMSkRHZjIbIaIoMX1x6qAqA0kqWaDMQAZn066tnFWCkopL1fRlpLbrKoOgwyAqWBxKDrQfZFiUXz+B2Ma0lSksQdRPP0vpkNmS5aNJJI8rSxxqJZw1a5ghdgSyQskLrklawjGcFXiUQyiOdHUcXkr7IONJapBPUvRdEZZlalUyuClqppJFagkGJPyjwCokwil7dE6QeMmQYypQ1jOYZe7ubRkrCAk8fej8/+PY7MXmBzTNsUVD1evSff47s+LH6+MYnUIcPs1yVhN/+NuejkKWRFsLWneGMkixPjFN6HmF/UN/+qgyl1lRaUSl11THRpZBoqdBSoqzA93xCP8QPIsLxFkEUrQ/WtzZgXxhGhGGI511+CHJrLeXCAmWnQ/lyTnU6u+R6FytkwSvRS/TTZcr+gCor0cIjkBGZGXA2P8LD7/8oq7P38+Rih8pYPnHfni3VJTz51RP1lf/uEWb3jDC7e5Qdu0fqSmwAIZiebrEw310vmQ16BX/1Zy/z8vPztEYC3vquOzlwaMclb3MBnD25wm999mmmZ9t4vsIaizGWHRb2W0EA/MbcCl7k8ehjd3D/m3aitMT0C9InzxA+shPZuP5BxJVELriRg8ijwC8lSXJow7LvAB9LkuTJq2x+J9sMIr/5bz/F7uxUHQxsSlNkr7pdtJGxggE+PUIGIiCVPpkIyKVPoTwqqamExFpQNsXkJXnWIOv6zAQps3qOGbWMLzysCkmlpI+lWwkG1qdvAvqlRyoCMuUjQoX1PbQ2zPTOUjR8+mMjBKQ0GND0SpqhJQrNVX84jAEpoZcqikpSlPWjrCRSWnqZpqoEpREUheTlk6N0Uo9B5VFZRa4FmScoq5BLFWqDKicwOb7NUaLEDAfYE7bugKeMQVuDXxWEJkPZeoiMsMq5c3CGnfk8lRKUSlApQaUlYmYauXMHTIxTeQrtBTz8vR9BDkdmrVZXsVWFDENkFFFmGd/+zrc48coxfM/HV9BUHTwt8QIPv9lAD2e8qx8Sz2uivQZe0Eb7DbTXZMfsDhYWU6wpKLIF/HDHNU1itJGtDNVKBqXBVgbKelBCtEQoUf/VEtnwEN7m96yqkrS3yqC7TL+zTNrvsOeeB2m0xy/zbpc36Oc8/fhJzp5c4dzZDqba2ndHKcEjj93Bm962F62vXFqqynrStUE/R0iBHD6EEGgBEYL23lEOP7obz3998veN4ILIBTf67axrMsyM1yQQOeN06cuQc3KSQnpkMqCUur6tYgEqbJVhipxOOo0XjBH4FWHYIwg6nF9RDIwGY5EGKq0wSgE+eNSPFpyiwSnuwlpDKYt6Ih8tqKSkkgIjZX1LSEiMUhipsTKilAWVKEhHMoRQTGUhEx2BV0XrxyGwKGXrCl9l0dKidLU+QqvW9VD1vgRPWkKvohmUyOFYSZfy6IGl9edWBCAbICMEfv2oFKLqoYsA34AOGphBSTGoB5ssqowihaIsKakH7CtsRWHqKYSLMicv6zkeFvKMBUYusRddWOjirQYEUYOxqZ1MTjbQnk9/9SSdwREG2VlEV6LzBtpvczj22NmYJ0/PY+1Fpbdq+KD+aEsuPZXr6W9fyFmG4yIrHQ4DTaseidZWWGvr97D1qLBrz42tKNIV/HBsLQeHLXw35vVwzgxr15+zYR17iWUb/x8JNBR0zzzPHfv/8SWO4ur23TEJ1BOwnX5lhTMnlylLs2k31y48ra1/dO5/aCcTU80tv8fH//73bGvfbjTT0+2rr3QbuJFLItfldtYXfvl/BT1GoFMCnaNMjiiLelyfrERYi7GAp7A7ItrLXbSoYNTDtv311kFCSoSor9KkAFNVWDSl1XQHConF8yoGmaKXKrK8voIrDZRGUlZiWHFc13NcrpVVzTLWKpieLBgJe0gLnjcGehQpJaPTDzA2fWg4TtCGrWyFqXJM2aPMVyjSedLVl6mqlMl9H2J6ZoqF+SWqsk/WX6TIupRZl7LoURU9TNXHmhRsjqBEqcvfvrtYUZi6stxCOpAUpY/WGql9hGwg9QjaD9C+j+83UErgh+3hJEcDtN9G6SY6GKfIztOZ+wpp5wgAQkVYk9W18BsJhRdM4oVTgKS//CwAY7s/QHP8AYSs88dUGaYcMP/y/4PFMDL7LpqRpNvpYaqMvH+aIlvCVinWlggV4kc7EMPmYYL1k2D4XFIVKyivDiB1AXHt89w449eFZWJj2lXWv7iTovLHaE+/9Q1pDnurXoG/VrdqPtxSJZEkSc7Fcfw08FHgs8O/T20hgFyTzshdiPQYxzshi/0J8lQSUIICLfpocvpyAmPaRGclUeDRCDRNfFplwPhom8mJUSbHxxhtN1GqLpIPBv31+QS2ylpLkadk/Q6DXofFM99k6ezT6PAAlWiSpwP6/Q5V/zzpK/OcOFby8NvHCSPFM984irEtmiMTNMeepTV6huboJJ4fUuYZRZFR5mn9fO3vxmVf+zRVmZH2uhR5esX91H6AHzbxg4ggahCEIUEU4AceXuDh+QqtSqQqEbaHF+4gIkMqH2tysv4pqnwFyIE+sAwM56UvoCrqwkK+etldAMCLdjC9/6MsHvtNst4JgtZdw1tOgsb4IZpj968HCoCq+H5OPfvzrJz+000/ulIFpNkiVdlh6s4fpjF+/yV/NKw1dM79e5ZP/wlShUzv/9Etf7aOc6u4YYPI0CeAz8Rx/DPUc8B9/I1+w7fc9wBPPdMk8ksmypRBtko2uo9Mj7CSlwzSkkFeUfQvrtSuK+LrRx3nlBREgSYKFJGvCQNNI9CEgaqX+8O09ef162bksXuqiRACP4jwg4j2+AzTu/dz6tkjRCOjTN7xN9bfOe0cIU+X0OE9LBz512AG3HXoHXRW+nRXFpg79jzHeitcjvYCtB/g+WF99e8FRK0xqOaY2nmAxuge/LBRP4LGhefD6UH7559G+SNkvVN0zv0VAON7P0g0coC8f5qguYeq7OGFM1hTMFhJkCpAqJCguXc4eZChrnWvKPNlst4pzp/8w7pEsUF75jGaE4cBgSm7nD/9ZxT9OuAUgzmy3it1AGnuY8eBH7/iZ628FiM73sHq3FfIeycJWheaj+bD/zNoX77PhBCSkR2Pobw2ZX6ZKQod5xZ3QweRJEmeB9723XzP2e5zvHfxt+sxsbTCf/f3E7zlva9ar6wMaV4xyMoNj4pBfuF1mlf0s5J0LS0rWeqkDBYuvK4uc7vtJz/8AG++d/O8CkIIvGBq/QcO6tLKuZc+C4DUDTD1HBkHH30P2h9bX68scvqrS5RFDnaAH7YJmhNoz988PLe1zB/5VdLVte44ZwiaHtN3fxSpAqw1LBz7DTpLJ1H+KHnv5CX3//wrf3C5mV9fxW/sYjb+u8OD1Ohgkvkjv4o1GSMzj9GYOMzZ538RgM65v2Js13uG+zwD5o8BGN/zQc6f+kNWzn4JgPbM27f03q2pR1md+wpzL36aoHUHzYkHaYwdIh/MobxRlI6u+n/UQc1xbk83dBC5HoJH/gP2fOBjV73fqZWkFUlaV+gFfDXWWsrKrAeUwbCk853j57n3jku3rinSeUw1IOvVP+LnXvzMepop+xvWW9gURLTnMzJZd5A78dR/D8DU/h/F82OsqTBVChhOPfsvXvWeWe8Ey6f/hIm9HyTtHGGw/BwAVbE5j3QwxfieD3D+5Bcos4VX/T+Xk/dPs3D015m664cBWDj6ecqsrsQf2/0+APY9/DPr+z1Yfp5o9CDzR/9finSO9vTbaE8/SnfhCYrhFKpB644tvbf2xxjd+X2snPkiWfc4Wfc4Syd+D7+xCy+c3PIxOM7tygWR60gIgacVnlaMNP315ffd+epOWmvG9/wAi8d/i7kX/s0V/+/5l3+Nxti99Je/jd/YRXP8MGW+vD4EOMDCkV/btI1Um2eHe+j7/inPfuXnqYpV8v5ZAM6f/KNXvdeuQ/8I7V9oSRXd/5MMVl5k+fSfMX33R9H+SN38tuigvPamXsmd+cdZPvVH9Je/w2D1ZaKRuxmsPH/JY9px8D9m7oV/w8KxX9+0vDnxEEBdyZ6eA6FedSxXMjr7bkZ2vIuse3S9VFcVHaTeeosjx7lduSByk4lGDlxy+cyBv40pB5hywNIrvwdU9Jfrdql5//SmW2CXU5dGQKiQPQ/8V2i/ya5D/4Azz30KawqsqdZLGHse/GmsrRBSI+WrS2PR6AGi0Qv7KoTYFGjWlo3MvB2wLJ/6Y+Zf/hxBc996+u7Dm5upBs09RKP3bgoyk3d8GL9Rl7C0X5felG6+5pZJQgjC9n52HPwJ5l74NFXRwYtmr76h49zm3MD7NxmhAsKLAsn0/h8jbN1BY+xeWlMPo4Mr34YZmX0XO+/7SRpj6/042XHgJxAqYGT2Xex98J+sd6QTQhG276Qqu+u3r7xoFql8lI4uGUBeq/b02/Ebu4D61tkapRuvWndjYJq55+M0Jx5cf62D197B7mJrgehy7+84zmauJHKTEUIwc/dH6S5+k6UTv4NUEdHowU3r7LzvJ1k58+eszv0l4cg9pKsvAeCFM8ze+/fWr9Kn7vohls9MEjR2EbT2sufwP7nkFbwQHqbsk3brSblGZ9/1uh+TF+3YVFradf8/uOS6jfEHWDrxewCE7Ts3pdX9P+rAt131Lay6Q6HyLtXZ0XGcjVwQuUk1xw+xdOJ3CC76IYX6R3ls13sY3fl9gOCVp/8HAHbe94lXrTu28/s2bXcpVdkFoDP/OFD3o3i9beww1xh/4LKlCik9mpMP0xx/9UizfmM3OphkfM8PbH8/1mZExNat3RzHuSIXRG5SQmp2H/7HiCvcTlpruju1/0dfU0XzxcZ2vZf++WdRukEBCPn6B5HRXe9B6gilW7Sm33rFdSf3feiSy6Xy2XX/T13zvkgVYKrBVUfNdRzHBZGb2lbv2TdG42t6H6nqvhJrw4q8ESURpRuM7Xp1f5zrQSgfqsGmlmyO41ya+5Y4V7VxqJD69fUfivuNtHZ8F49J5TjOq7kg4lzV7XZbZz143GbH7Tjb4b4lzpYo7/Yb9vp2C56Osx3uW+JsSdjef7134TpwXw/HuRr3LXG2RIgN9SK3/BX6cJ6OW/44HefauW+JszUbpoK9bSqcXRBxnKty3xJnSzb3Ar9dgshtcpyOcw1cEHG2ZPPtrNvjx1W4r4fjXNU1dzaM4/iTwHuBDOgC/3mSJF8fpn0R2AesTWz6L5Mk+fQw7SDwGWCSeu70jydJ8uLV0pzrQ8jbqSTimvg6zla9Ht+SfwccTpLkIeCfAb92Ufo/TJLkTcPHpzcs/xTwySRJDgKfBH5xi2nOdbCxJHLL14msxZDbpMTlONfimoNIkiS/nyRJMXz5VWBPHMdX/H/jOJ4BHgF+ZbjoV4BH4jievlLate6rs32beq3fNj+uriTiOFfzen9L/jPgD5IkMRuW/S9xHD8Tx/Fn4zjePVy2FziVJEkFMPx7erj8SmnOdXJ7Vay7Jr6Os1VXrROJ4/hJ6nqNS9mx9mMfx/GPAf8h8O4N6T+eJMkrcRwr4L+hvtX1zmvb5a2bnGxte9vp6duvh/alrOWDzFssbVgmlX/5jW5y8y9JCmBsvEVr7MJ54M6JmsuHmsuHmrDWXvN/EsfxR4B/Drw3SZJjl1mnDZwHfGAKeAGYTJKkGgaZReAA9WXgJdOSJJnf4i7dCRxdXOxizGs/vunpNvPznde83a1mYz70zj/L4rHfBGDvQ//tqwZlvJWcef7/pBicZcfBv0PQrAvP7pyouXyo3ar5IKVYu/i+Czi2pW2u9U3jOP7rwM8DH9gYQOI41nEc79iw6keBZ5IkMUmSnAOeHi5bS3sqSZL5K6Vd674627epie9tczvrVj9Ox7l2r8fl5KeBHPj1OF6ft+K9QAr8QRzHPvW38hTwYxu2+wTwmTiOf4a6hPLxLaY518GmOpFb/cfVVsMnt/hxOs7r4JqDSJIkV2o19eYrbPc88LbXmuZcH7dTP5H2zPewdOJ30MM52x3Hubxb98a28/ra2E/kFi+JtCYfojX50PXeDce5Kbg2jM6WbG7i6ziOU3NBxNmSW7k1luM42+eCiLMlm1tnOY7j1FwQcbZkc8W64zhOzQURZ0tcScRxnEtxQcTZElex7jjOpbgg4myNq1h3HOcSXBBxtsSVRBzHuRQXRJwtccOiO45zKe6XwXEcx9k2F0Qcx3GcbXNBxHEcx9k2F0Qcx3GcbXNBxHEcx9k2F5htw0MAAAVcSURBVEQcx3GcbbvmHmRxHP9b4H3AwnDR55Mk+dlh2g7gl6nnPB8A/2mSJI9fS5rjOI5z43i9SiI/lyTJm4aPn92w/J8Bf5EkyUHgp4DPxnEsrjHNcRzHuUG80bezfgT4FECSJH8JZFyYMne7aY7jOM4N4vUKIv9lHMfPxHH823Ec3wcQx/EkIJIkWdiw3glg73bTXqd9dbbJC2eu9y44jnODuWqdSBzHTwL7LpO8A/jvgDNJkpg4jj8O/GEcx/tfx33ctsnJ1ra3nZ5uv457cvPamA+Tk/8FWItU3nXco+vHnRM1lw81lw+1qwaRJEkeucoqpzas+0txHP8LYE+SJMfjOCaO46kNpYp9wCtJkixuJ+21HRosLnYxxr7WzZiebjM/33nN291qLp8P6Xd9X643d07UXD7UbtV8kFK85ovva76dFcfx7g3PPwBUXAgsnwc+MUx7JxAB37jGNMdxHOcG8XpMEvGZYZNcA6wCfyNJknKY9tPULav+NnVT3R9PksRcY5rjOI5zgxDWvvbbPTeBO4Gj7nbWtXH5cIHLi5rLh9qtmg8bbmfdBRzb0jZv5A45juM4tzYXRBzHcZxtc0HEcRzH2bbXo2L9RqSgvr+3Xdey7a3E5cMFLi9qLh9qt2I+bDgmtdVtbtWK9XcCX77eO+E4jnOTehfwl1tZ8VYNIgHwFuAMdb8Vx3Ec5+oUsBN4gnrMwqu6VYOI4ziO813gKtYdx3GcbXNBxHEcx9k2F0Qcx3GcbXNBxHEcx9k2F0Qcx3GcbXNBxHEcx9k2F0Qcx3GcbbtVhz3ZljiODwKfASaBReDjSZK8eH336o0Tx/Ex6mkK16Yq/K+TJPlCHMdvB36RejKwY8DHkiQ5N9zmsmk3iziO/znwQ9RTBhxOkuTZ4fLLfv7bTbuRXSEfjnGJ82KYdsudG3EcTwK/DNwN5MCLwN9LkmR+u8d7s+bFdriSyGafAj6ZJMlB4JPUJ8Gt7oeTJHnT8PGFOI4l8Fngp4b58BfAzwFcKe0m89vAu4HjFy2/0ue/3bQb2eXyAS46L+DKn/9Nfm5Y4H9OkiROkuQw8DLwc9s93ps8L14zF0SG4jieAR4BfmW46FeAR+I4nr5+e3VdPAqkSZKsjZvzKeBHtpB200iS5C+TJHll47Irff7bTXujj+NaXSofruKWPDeSJFlKkuSLGxb9e+AOtn+8N21ebIcLIhfsBU4lSVIBDP+eHi6/lX0ujuNvxXH8r+I4HgP2seHKNEmSBUDGcTxxlbSb3ZU+/+2m3cwuPi/gNjg3hqWIvw/8Lts/3lsiL7bKBZHb27uSJHmIerBKAfwf13l/nBvD7Xxe/O9Al9vrmK+JCyIXvALsjuNYAQz/7houvyWt3cpIkiQD/hXwDuAEdVEegDiOpwCTJMnSVdJudlf6/LebdlO6zHkBt/i5MWxocAD40SRJDNs/3ps+L14LF0SGhi0nngY+Olz0UeCpJEnmr99evXHiOG7GcTw6fC6AH6M+/m8AURzH7xyu+gng88PnV0q7qV3p899u2ndv718/Vzgv4BY+N+I4/h+p6zI+PAyesP3jvanz4rVyQ8FvEMfxvdRNNceB89RNNZPru1dvjDiO9wO/QT1/gAK+A/zDJEnOxHH8GHULo5ALzRPnhttdNu1mEcfx/wb8TWAWWAAWkyQ5dKXPf7tpN7JL5QPwIS5zXgy3ueXOjTiODwHPAi8Ag+Hio0mSfGS7x3uz5sV2uCDiOI7jbJu7neU4juNsmwsijuM4zra5IOI4juNsmwsijuM4zra5IOI4juNsmwsijuM4zra5IOI4juNsmwsijuM4zrb9//bjtBFReu7hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cur_read = conn.cursor(cursor_factory=RealDictCursor)\n",
    "cur_read.execute(\"SELECT * FROM public.conflicts;\")\n",
    "batch = cur_read.fetchall()\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "dim='2d'\n",
    "la_time = 1200\n",
    "bin_sec = 20\n",
    "bin_dp_df = {}\n",
    "spd_lim = 30000\n",
    "\n",
    "# plt.figure(figsize=(30,30))\n",
    "\n",
    "cfl_dst = 9260\n",
    "for b in batch:\n",
    "\n",
    "    try:\n",
    "        b = realign_conflict(b)\n",
    "    except Exception as e:\n",
    "        print('Error in realigning Conflict')\n",
    "        b = None\n",
    "        \n",
    "    if b:\n",
    "        b['ettc'] = []\n",
    "        b['ttc'] = []\n",
    "        b['ecpa'] = []\n",
    "\n",
    "        fl1 = pd.DataFrame()\n",
    "        fl1['lat'] = b['lat_1']\n",
    "        fl1['lon'] = b['lon_1']\n",
    "        fl1['ts'] = b['ts_1']\n",
    "        fl1['hdg'] = b['hdg_1']\n",
    "        fl1['spd'] = b['spd_1']\n",
    "\n",
    "        fl2 = pd.DataFrame()\n",
    "        fl2['lat'] = b['lat_2']\n",
    "        fl2['lon'] = b['lon_2']\n",
    "        fl2['ts'] = b['ts_2']\n",
    "        fl2['hdg'] = b['hdg_2']\n",
    "        fl2['spd'] = b['spd_2']\n",
    "\n",
    "\n",
    "        for t in range(len(b['ts_1'])):\n",
    "            try:\n",
    "                b['ttc'].append(b['ts_1'][-1] - b['ts_1'][t])\n",
    "            except:\n",
    "                print(b)\n",
    "            b['ettc'].append(ttc_est(fl1['lat'][t], fl1['lon'][t], fl2['lat'][t], fl2['lon'][t], \n",
    "                                     fl1['hdg'][t], fl2['hdg'][t], fl1['spd'][t], fl2['spd'][t]))\n",
    "        \n",
    "        b['ttc_diff'] = [x-y for x,y in zip(b['ttc'],b['ettc'])]\n",
    "            \n",
    "            \n",
    "        for tt in range(int(la_time/bin_sec)):\n",
    "            bmin = tt*bin_sec\n",
    "            bmax = (tt+1)*bin_sec\n",
    "            if str(bmax) not in list(bin_dp_df.keys()):\n",
    "                bin_dp_df[str(bmax)] = []\n",
    "                \n",
    "            bin_dp_df[str(bmax)].extend([e for e,t in zip(b['ttc_diff'], b['ttc']) \n",
    "                                                if t >= bmin and t <= bmax])\n",
    "\n",
    "        plt.plot(b['ttc'], b['ettc'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51.63523 4.28078 51.91273 5.10262 29.1 271.3 400.0 425.0\n"
     ]
    }
   ],
   "source": [
    "t = len(fl1['lat']) - 100\n",
    "ttc_est(fl1['lat'][t], fl1['lon'][t], fl2['lat'][t], fl2['lon'][t], \n",
    "                                     fl1['hdg'][t], fl2['hdg'][t], fl1['spd'][t], fl2['spd'][t])\n",
    "lat_1, lon_1, lat_2, lon_2, hdg_1, hdg_2, spd_1, spd_2 = fl1['lat'][t], fl1['lon'][t], fl2['lat'][t], fl2['lon'][t], fl1['hdg'][t], fl2['hdg'][t], fl1['spd'][t], fl2['spd'][t]\n",
    "\n",
    "print(lat_1, lon_1, lat_2, lon_2, hdg_1, hdg_2, spd_1, spd_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "knots_to_ms = 0.514444\n",
    "ipz_lim = 9260 #meters\n",
    "m_per_deglat = 111200\n",
    "m_per_deglon = 68460\n",
    "dy_1 = math.cos(math.radians(heading_diff(hdg_1,0))) * spd_1 * knots_to_ms\n",
    "dy_2 = math.cos(math.radians(heading_diff(hdg_2,0))) * spd_2 * knots_to_ms\n",
    "dx_1 = math.sin(math.radians(heading_diff(hdg_1,0))) * spd_1 * knots_to_ms\n",
    "dx_2 = math.sin(math.radians(heading_diff(hdg_2,0))) * spd_2 * knots_to_ms\n",
    "\n",
    "dx = abs(dx_1 - dx_2)\n",
    "dy = abs(dy_1 - dy_2)\n",
    "\n",
    "s = calc_coord_dst((lat_1, lon_1),(lat_2, lon_2))\n",
    "# y = abs(lat_1 - lat_2) * m_per_deglat\n",
    "# x = abs(lon_1 - lon_2) * m_per_deglon\n",
    "x = math.sin(math.radians(heading_diff(calc_compass_bearing((lat_1, lon_1),(lat_2, lon_2)),0)))*s\n",
    "y = math.cos(math.radians(heading_diff(calc_compass_bearing((lat_1, lon_1),(lat_2, lon_2)),0)))*s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "151.75006184322987"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (s-ipz_lim)/np.sqrt((dy**2 + dx**2))\n",
    "ds = (2*abs(x)*dx + 2*abs(y)*dy) / (2*s)\n",
    "(s - ipz_lim) / ds\n",
    "(s - ipz_lim) / np.sqrt((dy**2 + dx**2))\n",
    "# fl2['ts'][2]\n",
    "# (b['ts_2'][-1] - b['ts_2'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "199.1746095406823"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(calc_coord_dst_simple((fl1['lon'][0],fl1['lat'][0]),(fl2['lon'][0],fl2['lat'][0])) - \n",
    "calc_coord_dst_simple((fl1['lon'][1],fl1['lat'][1]),(fl2['lon'][1],fl2['lat'][1])))/(fl1['ts'][1] - fl1['ts'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_data = []\n",
    "\n",
    "bin_df = bin_dp_df\n",
    "\n",
    "for k in [kx for kx in bin_df.keys() if int(kx) <= 1200]:\n",
    "    box_data.append((int(k), [i for i in bin_df[k] if not np.isnan(i)]))\n",
    "    \n",
    "box_data_sort = sorted(box_data, key=lambda tup: tup[0])\n",
    "box_data_2 = [i[1] for i in box_data_sort]\n",
    "\n",
    "x = range(len(box_data_2))\n",
    "\n",
    "plt.figure(figsize=(20,8))\n",
    "plt.boxplot(box_data_2, showfliers=False, patch_artist=True, whis=[5,95])\n",
    "# plt.hlines(9260, 0, 1200, colors='r', linestyles='dashed', label='IPZ limit')\n",
    "# plt.hlines(-9260, 0, 1200, colors='r', linestyles='dashed')\n",
    "# plt.hlines(-9260, 0, 1200, colors='r', linestyles='dashed', label='IPZ limit')\n",
    "plt.xticks(x, [i[0] for i in box_data_sort])\n",
    "plt.xticks(rotation=70)\n",
    "plt.xlabel('Look-ahead time (seconds)')\n",
    "plt.ylabel('TTC error in seconds')\n",
    "plt.title('Evolution of Time-to-Conflict error over look-ahead time')\n",
    "# plt.legend(prop={'size': 16})\n",
    "# plt.xlim((0,600))\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2496"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(bin_dp_df['800'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "def calc_coord_dst(lon1, lat1, lon2, lat2):\n",
    "    R = 6371.1 * 1000  # Radius of the Earth in m\n",
    "\n",
    "    [lon1, lat1, lon2, lat2] = [math.radians(l) for l in [lon1, lat1, lon2, lat2]]\n",
    "\n",
    "    dlon = lon2 - lon1\n",
    "    dlat = lat2 - lat1\n",
    "\n",
    "    a = math.sin(dlat / 2) ** 2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2) ** 2\n",
    "    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))\n",
    "\n",
    "    d = R * c\n",
    "    return d\n",
    "\n",
    "cur_read = conn.cursor(cursor_factory=RealDictCursor)\n",
    "cur_read.execute(\"SELECT ts_1, lat_1, lon_1, alt_1, ts_2, lat_2, lon_2, alt_2, dstd \\\n",
    "                 FROM public.conflicts_2 WHERE dstd < 2000 AND altd < 5000 AND td < 10 LIMIT 100;\")\n",
    "batch = cur_read.fetchall()\n",
    "\n",
    "for b in batch:\n",
    "    \n",
    "    df1 = pd.DataFrame()\n",
    "    for k in ['ts_1', 'lat_1', 'lon_1', 'alt_1']:\n",
    "        df1[k.strip('_1')] = b[k]\n",
    "    df2 = pd.DataFrame()\n",
    "    for k in ['ts_2', 'lat_2', 'lon_2', 'alt_2']:\n",
    "        df2[k.strip('_2')] = b[k]\n",
    "        \n",
    "    df1['ts'] = df1['ts'].astype(int)\n",
    "    df2['ts'] = df2['ts'].astype(int)\n",
    "    \n",
    "    if df1['ts'].max() > df2['ts'].max():\n",
    "        df_l = df1\n",
    "        df_r = df2\n",
    "        df_r['ts_n'] = df_r['ts'].astype(int)\n",
    "        sfx = ('_1','_2')\n",
    "    else:\n",
    "        df_l = df2\n",
    "        df_r = df1\n",
    "        df_r['ts_n'] = df_r['ts'].astype(int)\n",
    "        sfx = ('_2','_1')\n",
    "\n",
    "    print(len(df1))\n",
    "    print(len(df2))\n",
    "    dfm = pd.merge_asof(df_l, df_r, on='ts', direction='nearest', tolerance=10, suffixes=sfx)\n",
    "\n",
    "\n",
    "    dfm['td'] = dfm['ts']-dfm['ts_n']\n",
    "    dfm = dfm.dropna(how='any')\n",
    "    dfm['dstd'] = dfm.apply(lambda r: calc_coord_dst(r['lon_1'], r['lat_1'], r['lon_2'], r['lat_2']), axis=1)\n",
    "    print(dfm['dstd'].iloc[-1])\n",
    "    \n",
    "#     fig = plt.figure(figsize=(30,30))\n",
    "#     plt.scatter(dfm['lon_1'], dfm['lat_1'], s=2, alpha=0.5)\n",
    "#     plt.scatter(dfm['lon_2'], dfm['lat_2'], s=2, alpha=0.5)\n",
    "#     plt.show()\n",
    "#     fig = plt.figure(figsize=(30,30))\n",
    "#     plt.scatter(b['lon_1'], b['lat_1'], s=2, alpha=0.5)\n",
    "#     plt.scatter(b['lon_2'], b['lat_2'], s=2, alpha=0.5)\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "def save_obj(obj, name ):\n",
    "    with open(name + '.pkl', 'wb') as f:\n",
    "        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_obj(bin_dp_df,'deterministic_dict')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cur_read.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from operator import itemgetter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1crd = [(1,2,3),(2,3,6),(3,4,5)]\n",
    "max(f1crd, key=itemgetter(2))[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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